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Yang M, Peng Z, van Pul C, Andriessen P, Dong K, Silvertand D, Li J, Liu C, Long X. Continuous prediction and clinical alarm management of late-onset sepsis in preterm infants using vital signs from a patient monitor. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 255:108335. [PMID: 39047574 DOI: 10.1016/j.cmpb.2024.108335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2024] [Revised: 06/14/2024] [Accepted: 07/17/2024] [Indexed: 07/27/2024]
Abstract
BACKGROUND AND OBJECTIVE Continuous prediction of late-onset sepsis (LOS) could be helpful for improving clinical outcomes in neonatal intensive care units (NICU). This study aimed to develop an artificial intelligence (AI) model for assisting the bedside clinicians in successfully identifying infants at risk for LOS using non-invasive vital signs monitoring. METHODS In a retrospective study from the NICU of the Máxima Medical Center in Veldhoven, the Netherlands, a total of 492 preterm infants less than 32 weeks gestation were included between July 2016 and December 2018. Data on heart rate (HR), respiratory rate (RR), and oxygen saturation (SpO2) at 1 Hz were extracted from the patient monitor. We developed multiple AI models using 102 extracted features or raw time series to provide hourly LOS risk prediction. Shapley values were used to explain the model. For the best performing model, the effect of different vital signs and also the input type of signals on model performance was tested. To further assess the performance of applying the best performing model in a real-world clinical setting, we performed a simulation using four different alarm policies on continuous real-time predictions starting from three days after birth. RESULTS A total of 51 LOS patients and 68 controls were finally included according to the patient inclusion and exclusion criteria. When tested by seven-fold cross-validations, the mean (standard deviation) area under the receiver operating characteristic curve (AUC) six hours before CRASH was 0.875 (0.072) for the best performing model, compared to the other six models with AUC ranging from 0.782 (0.089) to 0.846 (0.083). The best performing model performed only slightly worse than the model learning from raw physiological waveforms (0.886 [0.068]), successfully detecting 96.1 % of LOS patients before CRASH. When setting the expected alarm window to 24 h and using a multi-threshold alarm policy, the sensitivity metric was 71.6 %, while the positive predictive value was 9.9 %, resulting in an average of 1.15 alarms per day per patient. CONCLUSIONS The proposed AI model, which learns from routinely collected vital signs, has the potential to assist clinicians in the early detection of LOS. Combined with interpretability and clinical alarm management, this model could be better translated into medical practice for future clinical implementation.
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Affiliation(s)
- Meicheng Yang
- State Key Laboratory of Digital Medical Engineering, School of Instrument Science and Engineering, Southeast University, Nanjing, China; Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Zheng Peng
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Department of Clinical Physics, Máxima Medical Centre, Veldhoven, the Netherlands
| | - Carola van Pul
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Department of Clinical Physics, Máxima Medical Centre, Veldhoven, the Netherlands; Department of Applied Physics, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Peter Andriessen
- Department of Applied Physics, Eindhoven University of Technology, Eindhoven, the Netherlands; Department of Pediatrics, Máxima Medical Centre, Veldhoven, the Netherlands
| | - Kejun Dong
- Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, United States of America
| | - Demi Silvertand
- Department of Pediatrics, Máxima Medical Centre, Veldhoven, the Netherlands
| | - Jianqing Li
- State Key Laboratory of Digital Medical Engineering, School of Instrument Science and Engineering, Southeast University, Nanjing, China; School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Chengyu Liu
- State Key Laboratory of Digital Medical Engineering, School of Instrument Science and Engineering, Southeast University, Nanjing, China
| | - Xi Long
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands.
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Zhao Y, Coppola A, Karamchandani U, Amiras D, Gupte CM. Artificial intelligence applied to magnetic resonance imaging reliably detects the presence, but not the location, of meniscus tears: a systematic review and meta-analysis. Eur Radiol 2024; 34:5954-5964. [PMID: 38386028 PMCID: PMC11364796 DOI: 10.1007/s00330-024-10625-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2023] [Revised: 12/24/2023] [Accepted: 01/13/2024] [Indexed: 02/23/2024]
Abstract
OBJECTIVES To review and compare the accuracy of convolutional neural networks (CNN) for the diagnosis of meniscal tears in the current literature and analyze the decision-making processes utilized by these CNN algorithms. MATERIALS AND METHODS PubMed, MEDLINE, EMBASE, and Cochrane databases up to December 2022 were searched in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) statement. Risk of analysis was used for all identified articles. Predictive performance values, including sensitivity and specificity, were extracted for quantitative analysis. The meta-analysis was divided between AI prediction models identifying the presence of meniscus tears and the location of meniscus tears. RESULTS Eleven articles were included in the final review, with a total of 13,467 patients and 57,551 images. Heterogeneity was statistically significantly large for the sensitivity of the tear identification analysis (I2 = 79%). A higher level of accuracy was observed in identifying the presence of a meniscal tear over locating tears in specific regions of the meniscus (AUC, 0.939 vs 0.905). Pooled sensitivity and specificity were 0.87 (95% confidence interval (CI) 0.80-0.91) and 0.89 (95% CI 0.83-0.93) for meniscus tear identification and 0.88 (95% CI 0.82-0.91) and 0.84 (95% CI 0.81-0.85) for locating the tears. CONCLUSIONS AI prediction models achieved favorable performance in the diagnosis, but not location, of meniscus tears. Further studies on the clinical utilities of deep learning should include standardized reporting, external validation, and full reports of the predictive performances of these models, with a view to localizing tears more accurately. CLINICAL RELEVANCE STATEMENT Meniscus tears are hard to diagnose in the knee magnetic resonance images. AI prediction models may play an important role in improving the diagnostic accuracy of clinicians and radiologists. KEY POINTS • Artificial intelligence (AI) provides great potential in improving the diagnosis of meniscus tears. • The pooled diagnostic performance for artificial intelligence (AI) in identifying meniscus tears was better (sensitivity 87%, specificity 89%) than locating the tears (sensitivity 88%, specificity 84%). • AI is good at confirming the diagnosis of meniscus tears, but future work is required to guide the management of the disease.
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Affiliation(s)
- Yi Zhao
- Imperial College London School of Medicine, Exhibition Rd, South Kensington, London, SW7 2BU, UK.
| | - Andrew Coppola
- Imperial College London School of Medicine, Exhibition Rd, South Kensington, London, SW7 2BU, UK
| | | | - Dimitri Amiras
- Imperial College London School of Medicine, Exhibition Rd, South Kensington, London, SW7 2BU, UK
- Imperial College London NHS Trust, London, UK
| | - Chinmay M Gupte
- Imperial College London School of Medicine, Exhibition Rd, South Kensington, London, SW7 2BU, UK
- Imperial College London NHS Trust, London, UK
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McClymont H, Lambert SB, Barr I, Vardoulakis S, Bambrick H, Hu W. Internet-based Surveillance Systems and Infectious Diseases Prediction: An Updated Review of the Last 10 Years and Lessons from the COVID-19 Pandemic. J Epidemiol Glob Health 2024:10.1007/s44197-024-00272-y. [PMID: 39141074 DOI: 10.1007/s44197-024-00272-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Accepted: 06/26/2024] [Indexed: 08/15/2024] Open
Abstract
The last decade has seen major advances and growth in internet-based surveillance for infectious diseases through advanced computational capacity, growing adoption of smart devices, increased availability of Artificial Intelligence (AI), alongside environmental pressures including climate and land use change contributing to increased threat and spread of pandemics and emerging infectious diseases. With the increasing burden of infectious diseases and the COVID-19 pandemic, the need for developing novel technologies and integrating internet-based data approaches to improving infectious disease surveillance is greater than ever. In this systematic review, we searched the scientific literature for research on internet-based or digital surveillance for influenza, dengue fever and COVID-19 from 2013 to 2023. We have provided an overview of recent internet-based surveillance research for emerging infectious diseases (EID), describing changes in the digital landscape, with recommendations for future research directed at public health policymakers, healthcare providers, and government health departments to enhance traditional surveillance for detecting, monitoring, reporting, and responding to influenza, dengue, and COVID-19.
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Affiliation(s)
- Hannah McClymont
- Ecosystem Change and Population Health (ECAPH) Research Group, School of Public Health and Social Work, Queensland University of Technology (QUT), Brisbane, Australia
| | - Stephen B Lambert
- Communicable Diseases Branch, Queensland Health, Brisbane, Australia
- National Centre for Immunisation Research and Surveillance, Sydney Children's Hospitals Network, Westmead, Australia
| | - Ian Barr
- WHO Collaborating Centre for Reference and Research on Influenza, The Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
- Department of Microbiology and Immunology, University of Melbourne, Melbourne, Australia
| | - Sotiris Vardoulakis
- Health Research Institute, University of Canberra, Canberra, Australia
- Healthy Environments and Lives (HEAL) National Research Network, Canberra, Australia
| | - Hilary Bambrick
- National Centre for Epidemiology and Population Health, College of Health and Medicine, The Australian National University, Canberra, Australia
| | - Wenbiao Hu
- Ecosystem Change and Population Health (ECAPH) Research Group, School of Public Health and Social Work, Queensland University of Technology (QUT), Brisbane, Australia.
- Healthy Environments and Lives (HEAL) National Research Network, Canberra, Australia.
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Pelletier ED, Jeffries SD, Song K, Hemmerling TM. Comparative Analysis of Machine-Learning Model Performance in Image Analysis: The Impact of Dataset Diversity and Size. Anesth Analg 2024:00000539-990000000-00901. [PMID: 39116018 DOI: 10.1213/ane.0000000000007088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/10/2024]
Abstract
BACKGROUND This study presents an analysis of machine-learning model performance in image analysis, with a specific focus on videolaryngoscopy procedures. The research aimed to explore how dataset diversity and size affect the performance of machine-learning models, an issue vital to the advancement of clinical artificial intelligence tools. METHODS A total of 377 videolaryngoscopy videos from YouTube were used to create 6 varied datasets, each differing in patient diversity and image count. The study also incorporates data augmentation techniques to enhance these datasets further. Two machine-learning models, YOLOv5-Small and YOLOv8-Small, were trained and evaluated on metrics such as F1 score (a statistical measure that combines the precision and recall of the model into a single metric, reflecting its overall accuracy), precision, recall, mAP@50, and mAP@50-95. RESULTS The findings indicate a significant impact of dataset configuration on model performance, especially the balance between diversity and quantity. The Multi-25 × 10 dataset, featuring 25 images from 10 different patients, demonstrates superior performance, highlighting the value of a well-balanced dataset. The study also finds that the effects of data augmentation vary across different types of datasets. CONCLUSIONS Overall, this study emphasizes the critical role of dataset structure in the performance of machine-learning models in medical image analysis. It underscores the necessity of striking an optimal balance between dataset size and diversity, thereby illuminating the complexities inherent in data-driven machine-learning development.
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Affiliation(s)
- Eric D Pelletier
- From the Department of Experimental Surgery, McGill University Health Center, Montreal, Quebec, Canada
| | - Sean D Jeffries
- From the Department of Experimental Surgery, McGill University Health Center, Montreal, Quebec, Canada
- Department of Anesthesia, McGill University, Montreal, Quebec, Canada
| | - Kevin Song
- Department of Anesthesia, McGill University, Montreal, Quebec, Canada
| | - Thomas M Hemmerling
- From the Department of Experimental Surgery, McGill University Health Center, Montreal, Quebec, Canada
- Department of Anesthesia, McGill University, Montreal, Quebec, Canada
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Lee JY, Park J, Choi H, Oh EG. Nursing Variables Predicting Readmissions in Patients With a High Risk: A Scoping Review. Comput Inform Nurs 2024:00024665-990000000-00213. [PMID: 39093059 DOI: 10.1097/cin.0000000000001172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
Unplanned readmission endangers patient safety and increases unnecessary healthcare expenditure. Identifying nursing variables that predict patient readmissions can aid nurses in providing timely nursing interventions that help patients avoid readmission after discharge. We aimed to provide an overview of the nursing variables predicting readmission of patients with a high risk. The authors searched five databases-PubMed, CINAHL, EMBASE, Cochrane Library, and Scopus-for publications from inception to April 2023. Search terms included "readmission" and "nursing records." Eight studies were included for review. Nursing variables were classified into three categories-specifically, nursing assessment, nursing diagnosis, and nursing intervention. The nursing assessment category comprised 75% of the nursing variables; the proportions of the nursing diagnosis (25%) and nursing intervention categories (12.5%) were relatively low. Although most variables of the nursing assessment category focused on the patients' physical aspect, emotional and social aspects were also considered. This study demonstrated how nursing care contributes to patients' adverse outcomes. The findings can assist nurses in identifying the essential nursing assessment, diagnosis, and interventions, which should be provided from the time of patients' admission. This can mitigate preventable readmissions of patients with a high risk and facilitate their safe transition from an acute care setting to the community.
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Affiliation(s)
- Ji Yea Lee
- Author Affiliations: College of Nursing, Ajou University (Ms Lee), Suwon; and College of Nursing, Yonsei University (Ms Park and Dr Oh), Seoul, South Korea; College of Nursing, University of Illinois Chicago (Ms Choi); and Mo-Im Kim Nursing Research Institute, College of Nursing, Yonsei University (Dr Oh); Yonsei Evidence-Based Nursing Centre of Korea: A Joanna Briggs Institute Affiliated Group (Dr Oh); and Institute for Innovation in Digital Healthcare, Yonsei University (Dr Oh), Seoul, South Korea
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Meerwijk EL, McElfresh DC, Martins S, Tamang SR. Evaluating accuracy and fairness of clinical decision support algorithms when health care resources are limited. J Biomed Inform 2024; 156:104664. [PMID: 38851413 DOI: 10.1016/j.jbi.2024.104664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 04/02/2024] [Accepted: 06/02/2024] [Indexed: 06/10/2024]
Abstract
OBJECTIVE Guidance on how to evaluate accuracy and algorithmic fairness across subgroups is missing for clinical models that flag patients for an intervention but when health care resources to administer that intervention are limited. We aimed to propose a framework of metrics that would fit this specific use case. METHODS We evaluated the following metrics and applied them to a Veterans Health Administration clinical model that flags patients for intervention who are at risk of overdose or a suicidal event among outpatients who were prescribed opioids (N = 405,817): Receiver - Operating Characteristic and area under the curve, precision - recall curve, calibration - reliability curve, false positive rate, false negative rate, and false omission rate. In addition, we developed a new approach to visualize false positives and false negatives that we named 'per true positive bars.' We demonstrate the utility of these metrics to our use case for three cohorts of patients at the highest risk (top 0.5 %, 1.0 %, and 5.0 %) by evaluating algorithmic fairness across the following age groups: <=30, 31-50, 51-65, and >65 years old. RESULTS Metrics that allowed us to assess group differences more clearly were the false positive rate, false negative rate, false omission rate, and the new 'per true positive bars'. Metrics with limited utility to our use case were the Receiver - Operating Characteristic and area under the curve, the calibration - reliability curve, and the precision - recall curve. CONCLUSION There is no "one size fits all" approach to model performance monitoring and bias analysis. Our work informs future researchers and clinicians who seek to evaluate accuracy and fairness of predictive models that identify patients to intervene on in the context of limited health care resources. In terms of ease of interpretation and utility for our use case, the new 'per true positive bars' may be the most intuitive to a range of stakeholders and facilitates choosing a threshold that allows weighing false positives against false negatives, which is especially important when predicting severe adverse events.
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Affiliation(s)
- Esther L Meerwijk
- Program Evaluation and Resource Center, Office of Mental Health and Suicide Prevention, Department of Veterans Affairs, Menlo Park, CA, USA; VA Health Systems Research, Center for Innovation to Implementation (Ci2i), VA Palo Alto Health Care System, Menlo Park, CA, USA.
| | - Duncan C McElfresh
- Program Evaluation and Resource Center, Office of Mental Health and Suicide Prevention, Department of Veterans Affairs, Menlo Park, CA, USA
| | - Susana Martins
- Program Evaluation and Resource Center, Office of Mental Health and Suicide Prevention, Department of Veterans Affairs, Menlo Park, CA, USA
| | - Suzanne R Tamang
- Program Evaluation and Resource Center, Office of Mental Health and Suicide Prevention, Department of Veterans Affairs, Menlo Park, CA, USA; VA Health Systems Research, Center for Innovation to Implementation (Ci2i), VA Palo Alto Health Care System, Menlo Park, CA, USA; Department of Medicine, Stanford University, Stanford, CA, USA
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la Roi-Teeuw HM, van Royen FS, de Hond A, Zahra A, de Vries S, Bartels R, Carriero AJ, van Doorn S, Dunias ZS, Kant I, Leeuwenberg T, Peters R, Veerhoek L, van Smeden M, Luijken K. Don't be misled: 3 misconceptions about external validation of clinical prediction models. J Clin Epidemiol 2024; 172:111387. [PMID: 38729274 DOI: 10.1016/j.jclinepi.2024.111387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 04/24/2024] [Accepted: 05/02/2024] [Indexed: 05/12/2024]
Abstract
Clinical prediction models provide risks of health outcomes that can inform patients and support medical decisions. However, most models never make it to actual implementation in practice. A commonly heard reason for this lack of implementation is that prediction models are often not externally validated. While we generally encourage external validation, we argue that an external validation is often neither sufficient nor required as an essential step before implementation. As such, any available external validation should not be perceived as a license for model implementation. We clarify this argument by discussing 3 common misconceptions about external validation. We argue that there is not one type of recommended validation design, not always a necessity for external validation, and sometimes a need for multiple external validations. The insights from this paper can help readers to consider, design, interpret, and appreciate external validation studies.
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Affiliation(s)
- Hannah M la Roi-Teeuw
- Department of General Practice and Nursing Science, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands.
| | - Florien S van Royen
- Department of General Practice and Nursing Science, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands
| | - Anne de Hond
- Department of Epidemiology and Health Economics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands
| | - Anum Zahra
- Department of Epidemiology and Health Economics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands
| | - Sjoerd de Vries
- Department of Digital Health, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands; Department of Information and Computing Sciences, Utrecht University, Princetonplein 5, 3584 CC, Utrecht, The Netherlands
| | - Richard Bartels
- Department of Digital Health, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands; Department of Data Science and Biostatistics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands
| | - Alex J Carriero
- Department of Epidemiology and Health Economics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands
| | - Sander van Doorn
- Department of General Practice and Nursing Science, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands
| | - Zoë S Dunias
- Department of Epidemiology and Health Economics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands
| | - Ilse Kant
- Department of Digital Health, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands
| | - Tuur Leeuwenberg
- Department of Epidemiology and Health Economics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands
| | - Ruben Peters
- Department of Digital Health, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands
| | - Laura Veerhoek
- Department of Digital Health, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands
| | - Maarten van Smeden
- Department of Epidemiology and Health Economics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands; Department of Data Science and Biostatistics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands
| | - Kim Luijken
- Department of Epidemiology and Health Economics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands
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Lukkien DRM, Stolwijk NE, Ipakchian Askari S, Hofstede BM, Nap HH, Boon WPC, Peine A, Moors EHM, Minkman MMN. AI-Assisted Decision-Making in Long-Term Care: Qualitative Study on Prerequisites for Responsible Innovation. JMIR Nurs 2024; 7:e55962. [PMID: 39052315 PMCID: PMC11310645 DOI: 10.2196/55962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Revised: 04/16/2024] [Accepted: 05/24/2024] [Indexed: 07/27/2024] Open
Abstract
BACKGROUND Although the use of artificial intelligence (AI)-based technologies, such as AI-based decision support systems (AI-DSSs), can help sustain and improve the quality and efficiency of care, their deployment creates ethical and social challenges. In recent years, a growing prevalence of high-level guidelines and frameworks for responsible AI innovation has been observed. However, few studies have specified the responsible embedding of AI-based technologies, such as AI-DSSs, in specific contexts, such as the nursing process in long-term care (LTC) for older adults. OBJECTIVE Prerequisites for responsible AI-assisted decision-making in nursing practice were explored from the perspectives of nurses and other professional stakeholders in LTC. METHODS Semistructured interviews were conducted with 24 care professionals in Dutch LTC, including nurses, care coordinators, data specialists, and care centralists. A total of 2 imaginary scenarios about AI-DSSs were developed beforehand and used to enable participants articulate their expectations regarding the opportunities and risks of AI-assisted decision-making. In addition, 6 high-level principles for responsible AI were used as probing themes to evoke further consideration of the risks associated with using AI-DSSs in LTC. Furthermore, the participants were asked to brainstorm possible strategies and actions in the design, implementation, and use of AI-DSSs to address or mitigate these risks. A thematic analysis was performed to identify the opportunities and risks of AI-assisted decision-making in nursing practice and the associated prerequisites for responsible innovation in this area. RESULTS The stance of care professionals on the use of AI-DSSs is not a matter of purely positive or negative expectations but rather a nuanced interplay of positive and negative elements that lead to a weighed perception of the prerequisites for responsible AI-assisted decision-making. Both opportunities and risks were identified in relation to the early identification of care needs, guidance in devising care strategies, shared decision-making, and the workload of and work experience of caregivers. To optimally balance the opportunities and risks of AI-assisted decision-making, seven categories of prerequisites for responsible AI-assisted decision-making in nursing practice were identified: (1) regular deliberation on data collection; (2) a balanced proactive nature of AI-DSSs; (3) incremental advancements aligned with trust and experience; (4) customization for all user groups, including clients and caregivers; (5) measures to counteract bias and narrow perspectives; (6) human-centric learning loops; and (7) the routinization of using AI-DSSs. CONCLUSIONS The opportunities of AI-assisted decision-making in nursing practice could turn into drawbacks depending on the specific shaping of the design and deployment of AI-DSSs. Therefore, we recommend considering the responsible use of AI-DSSs as a balancing act. Moreover, considering the interrelatedness of the identified prerequisites, we call for various actors, including developers and users of AI-DSSs, to cohesively address the different factors important to the responsible embedding of AI-DSSs in practice.
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Affiliation(s)
- Dirk R M Lukkien
- Vilans Centre of Expertise of Long Term Care, Utrecht, Netherlands
- Copernicus Institute of Sustainable Development, Utrecht University, Utrecht, Netherlands
| | | | - Sima Ipakchian Askari
- Vilans Centre of Expertise of Long Term Care, Utrecht, Netherlands
- Human Technology Interaction, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Bob M Hofstede
- Vilans Centre of Expertise of Long Term Care, Utrecht, Netherlands
- Human Technology Interaction, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Henk Herman Nap
- Vilans Centre of Expertise of Long Term Care, Utrecht, Netherlands
- Human Technology Interaction, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Wouter P C Boon
- Copernicus Institute of Sustainable Development, Utrecht University, Utrecht, Netherlands
| | - Alexander Peine
- Faculty of Humanities, Open University of The Netherlands, Heerlen, Netherlands
| | - Ellen H M Moors
- Copernicus Institute of Sustainable Development, Utrecht University, Utrecht, Netherlands
| | - Mirella M N Minkman
- Vilans Centre of Expertise of Long Term Care, Utrecht, Netherlands
- TIAS School for Business and Society, Tilburg University, Tilburg, Netherlands
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Pahlevani M, Taghavi M, Vanberkel P. A systematic literature review of predicting patient discharges using statistical methods and machine learning. Health Care Manag Sci 2024:10.1007/s10729-024-09682-7. [PMID: 39037567 DOI: 10.1007/s10729-024-09682-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 06/29/2024] [Indexed: 07/23/2024]
Abstract
Discharge planning is integral to patient flow as delays can lead to hospital-wide congestion. Because a structured discharge plan can reduce hospital length of stay while enhancing patient satisfaction, this topic has caught the interest of many healthcare professionals and researchers. Predicting discharge outcomes, such as destination and time, is crucial in discharge planning by helping healthcare providers anticipate patient needs and resource requirements. This article examines the literature on the prediction of various discharge outcomes. Our review discovered papers that explore the use of prediction models to forecast the time, volume, and destination of discharged patients. Of the 101 reviewed papers, 49.5% looked at the prediction with machine learning tools, and 50.5% focused on prediction with statistical methods. The fact that knowing discharge outcomes in advance affects operational, tactical, medical, and administrative aspects is a frequent theme in the papers studied. Furthermore, conducting system-wide optimization, predicting the time and destination of patients after discharge, and addressing the primary causes of discharge delay in the process are among the recommendations for further research in this field.
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Affiliation(s)
- Mahsa Pahlevani
- Department of Industrial Engineering, Dalhousie University, 5269 Morris Street, Halifax, B3H 4R2, NS, Canada
| | - Majid Taghavi
- Department of Industrial Engineering, Dalhousie University, 5269 Morris Street, Halifax, B3H 4R2, NS, Canada
- Sobey School of Business, Saint Mary's University, 923 Robie, Halifax, B3H 3C3, NS, Canada
| | - Peter Vanberkel
- Department of Industrial Engineering, Dalhousie University, 5269 Morris Street, Halifax, B3H 4R2, NS, Canada.
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Orive D, Echepare M, Bernasconi-Bisio F, Sanmamed MF, Pineda-Lucena A, de la Calle-Arroyo C, Detterbeck F, Hung RJ, Johansson M, Robbins HA, Seijo LM, Montuenga LM, Valencia K. Protein Biomarkers in Lung Cancer Screening: Technical Considerations and Feasibility Assessment. Arch Bronconeumol 2024:S0300-2896(24)00269-2. [PMID: 39079848 DOI: 10.1016/j.arbres.2024.07.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 06/28/2024] [Accepted: 07/12/2024] [Indexed: 08/25/2024]
Abstract
Lung cancer remains the leading cause of cancer-related deaths worldwide, mainly due to late diagnosis and the presence of metastases. Several countries around the world have adopted nation-wide LDCT-based lung cancer screening that will benefit patients, shifting the stage at diagnosis to earlier stages with more therapeutic options. Biomarkers can help to optimize the screening process, as well as refine the TNM stratification of lung cancer patients, providing information regarding prognostics and recommending management strategies. Moreover, novel adjuvant strategies will clearly benefit from previous knowledge of the potential aggressiveness and biological traits of a given early-stage surgically resected tumor. This review focuses on proteins as promising biomarkers in the context of lung cancer screening. Despite great efforts, there are still no successful examples of biomarkers in lung cancer that have reached the clinics to be used in early detection and early management. Thus, the field of biomarkers in early lung cancer remains an evident unmet need. A more specific objective of this review is to present an up-to-date technical assessment of the potential use of protein biomarkers in early lung cancer detection and management. We provide an overview regarding the benefits, challenges, pitfalls and constraints in the development process of protein-based biomarkers. Additionally, we examine how a number of emerging protein analytical technologies may contribute to the optimization of novel robust biomarkers for screening and effective management of lung cancer.
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Affiliation(s)
- Daniel Orive
- Solid Tumors Program, CIMA-University of Navarra, Pamplona, Spain; Department of Pathology, Anatomy and Physiology, School of Medicine, University of Navarra, Pamplona, Spain; Consorcio de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain
| | - Mirari Echepare
- Solid Tumors Program, CIMA-University of Navarra, Pamplona, Spain; Department of Pathology, Anatomy and Physiology, School of Medicine, University of Navarra, Pamplona, Spain; Consorcio de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain; Navarra Health Research Institute (IDISNA), Pamplona, Spain
| | - Franco Bernasconi-Bisio
- Molecular Therapeutics Program, CIMA-University of Navarra, Pamplona, Spain; Department of Biochemistry and Genetics, School of Sciences, University of Navarra, Pamplona, Spain
| | - Miguel Fernández Sanmamed
- Consorcio de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain; Program of Immunology and Immunotherapy, CIMA-University of Navarra, Pamplona, Spain; Department of Oncology, Clínica Universidad de Navarra, Pamplona, Spain
| | - Antonio Pineda-Lucena
- Navarra Health Research Institute (IDISNA), Pamplona, Spain; Molecular Therapeutics Program, CIMA-University of Navarra, Pamplona, Spain; Department of Biochemistry and Genetics, School of Sciences, University of Navarra, Pamplona, Spain
| | - Carlos de la Calle-Arroyo
- Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), Universidad de Navarra, Pamplona, Spain
| | - Frank Detterbeck
- Division of Thoracic Surgery, Department of Surgery, Yale School of Medicine, New Haven, CT, USA
| | - Rayjean J Hung
- Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Canada; Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | | | | | - Luis M Seijo
- CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain; Pulmonary Department, Clínica Universidad de Navarra, Madrid, Spain
| | - Luis M Montuenga
- Solid Tumors Program, CIMA-University of Navarra, Pamplona, Spain; Department of Pathology, Anatomy and Physiology, School of Medicine, University of Navarra, Pamplona, Spain; Consorcio de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain; Navarra Health Research Institute (IDISNA), Pamplona, Spain.
| | - Karmele Valencia
- Solid Tumors Program, CIMA-University of Navarra, Pamplona, Spain; Consorcio de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain; Navarra Health Research Institute (IDISNA), Pamplona, Spain; Department of Biochemistry and Genetics, School of Sciences, University of Navarra, Pamplona, Spain.
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11
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Oikonomou EK, Khera R. Designing medical artificial intelligence systems for global use: focus on interoperability, scalability, and accessibility. Hellenic J Cardiol 2024:S1109-9666(24)00158-1. [PMID: 39025234 DOI: 10.1016/j.hjc.2024.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 06/21/2024] [Accepted: 07/02/2024] [Indexed: 07/20/2024] Open
Abstract
Advances in artificial intelligence (AI) and machine learning systems promise faster, more efficient, and more personalized care. While many of these models are built on the premise of improving access to the timely screening, diagnosis, and treatment of cardiovascular disease, their validity and accessibility across diverse and international cohorts remain unknown. In this mini-review article, we summarize key obstacles in the effort to design AI systems that will be scalable, accessible, and accurate across distinct geographical and temporal settings. We discuss representativeness, interoperability, quality assurance, and the importance of vendor-agnostic data types that will be available to end-users across the globe. These topics illustrate how the timely integration of these principles into AI development is crucial to maximizing the global benefits of AI in cardiology.
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Affiliation(s)
- Evangelos K Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA; Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT, USA; Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA; Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
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12
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Li K, Pirabhahar S, Thomsett M, Turner K, Wainstein M, Ha JT, Katz I. Use of kidney failure risk equation as a tool to evaluate referrals from primary care to specialist nephrology care. Intern Med J 2024; 54:1126-1135. [PMID: 38532529 DOI: 10.1111/imj.16377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 01/04/2024] [Indexed: 03/28/2024]
Abstract
BACKGROUND With rising costs and burden of chronic kidney disease (CKD), timely referral of patients to a kidney specialist is crucial. Currently, Kidney Health Australia (KHA) uses a 'heat map' based on severity and not future risk of kidney failure, whereas the kidney failure risk equation (KFRE) score predicts future risk of progression. AIMS Evaluate whether a KFRE score assists with timing of CKD referrals. METHODS Retrospective cohort of 2137 adult patients, referred to tertiary hospital outpatient nephrologist between 2012 and 2020, were analysed. Referrals were analysed for concordance with the KHA referral guidelines and, with the KFRE score, a recommended practice. RESULTS Of 2137 patients, 626 (29%) did not have urine albumin-to-creatinine ratio (UACR) measurement at referral. For those who had a UACR, the number who met KFRE preferred referral criteria was 36% less than KHA criteria. If the recommended KFRE score was used, then fewer older patients (≥40 years) needed referral. Positively, many diabetes patients were referred, even if their risk of kidney failure was low, and 29% had a KFRE over 3%. For patients evaluated meeting KFRE criteria, a larger proportion (76%) remained in follow-up, with only 8% being discharged. CONCLUSIONS KFRE could reduce referrals and be a useful tool to assist timely referrals. Using KFRE for triage may allow those patients with very low risk of future kidney failure not be referred, remaining longer in primary care, saving health resources and reducing patients' stress and wait times. Using KFRE encourages albuminuria measurement.
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Affiliation(s)
- Katherine Li
- Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia
| | - Saiyini Pirabhahar
- Department of Renal Medicine, St George Hospital, Sydney, New South Wales, Australia
| | - Max Thomsett
- Department of Renal Medicine, St George Hospital, Sydney, New South Wales, Australia
| | - Kylie Turner
- Department of Renal Medicine, St George Hospital, Sydney, New South Wales, Australia
| | - Marina Wainstein
- Faculty of Medicine, University of Queensland, Brisbane, Queensland, Australia
| | - Jeffrey T Ha
- Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia
- The George Institute for Global Health, Sydney, New South Wales, Australia
| | - Ivor Katz
- Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia
- Department of Renal Medicine, St George Hospital, Sydney, New South Wales, Australia
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13
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Oettl FC, Pareek A, Winkler PW, Zsidai B, Pruneski JA, Senorski EH, Kopf S, Ley C, Herbst E, Oeding JF, Grassi A, Hirschmann MT, Musahl V, Samuelsson K, Tischer T, Feldt R. A practical guide to the implementation of AI in orthopaedic research, Part 6: How to evaluate the performance of AI research? J Exp Orthop 2024; 11:e12039. [PMID: 38826500 PMCID: PMC11141501 DOI: 10.1002/jeo2.12039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Revised: 03/13/2024] [Accepted: 04/19/2024] [Indexed: 06/04/2024] Open
Abstract
Artificial intelligence's (AI) accelerating progress demands rigorous evaluation standards to ensure safe, effective integration into healthcare's high-stakes decisions. As AI increasingly enables prediction, analysis and judgement capabilities relevant to medicine, proper evaluation and interpretation are indispensable. Erroneous AI could endanger patients; thus, developing, validating and deploying medical AI demands adhering to strict, transparent standards centred on safety, ethics and responsible oversight. Core considerations include assessing performance on diverse real-world data, collaborating with domain experts, confirming model reliability and limitations, and advancing interpretability. Thoughtful selection of evaluation metrics suited to the clinical context along with testing on diverse data sets representing different populations improves generalisability. Partnering software engineers, data scientists and medical practitioners ground assessment in real needs. Journals must uphold reporting standards matching AI's societal impacts. With rigorous, holistic evaluation frameworks, AI can progress towards expanding healthcare access and quality. Level of Evidence Level V.
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Affiliation(s)
- Felix C. Oettl
- Hospital for Special SurgeryNew YorkNew YorkUSA
- Schulthess KlinikZurichSwitzerland
| | - Ayoosh Pareek
- Sports Medicine and Shoulder Institute, Hospital for Special SurgeryNew YorkNew YorkUSA
| | - Philipp W. Winkler
- Department for Orthopaedics and Traumatology, Kepler University Hospital GmbHJohannes Kepler University LinzLinzAustria
- Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska AcademyUniversity of GothenburgGothenburgSweden
- Sahlgrenska Sports Medicine CenterGöteborgSweden
| | - Bálint Zsidai
- Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska AcademyUniversity of GothenburgGothenburgSweden
- Sahlgrenska Sports Medicine CenterGöteborgSweden
| | - James A. Pruneski
- Department of Orthopaedic SurgeryTripler Army Medical CenterHonoluluHawaiiUSA
| | - Eric Hamrin Senorski
- Sahlgrenska Sports Medicine CenterGöteborgSweden
- Department of Health and Rehabilitation, Institute of Neuroscience and Physiology, Sahlgrenska AcademyUniversity of GothenburgGothenburgSweden
| | - Sebastian Kopf
- Center of Orthopaedics and Traumatology, University Hospital Brandenburg an der Havel, Brandenburg Medical School Theodor FontaneGermany
| | - Christophe Ley
- Department of MathematicsUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Elmar Herbst
- Department of Trauma, Hand and Reconstructive SurgeryUniversity Hospital MuensterMuensterGermany
| | - Jacob F. Oeding
- Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska AcademyUniversity of GothenburgGothenburgSweden
- Mayo Clinic Alix School of Medicine, Mayo ClinicRochesterMinnesotaUSA
| | - Alberto Grassi
- IIa Clinica Ortopedica e Traumatologica, IRCCS Istituto Ortopedico RizzoliBolognaItaly
| | - Michael T. Hirschmann
- Department of Orthopaedic Surgery and TraumatologyKantonsspital BasellandBruderholzSwitzerland
- University of BaselBaselSwitzerland
| | - Volker Musahl
- Department of Orthopaedic Surgery, UPMC Freddie Fu Sports Medicine CenterUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Kristian Samuelsson
- Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska AcademyUniversity of GothenburgGothenburgSweden
- Sahlgrenska Sports Medicine CenterGöteborgSweden
- Department of OrthopaedicsSahlgrenska University HospitalMölndalSweden
| | - Thomas Tischer
- Department of Orthopaedic SurgeryUniversitymedicine RostockRostockGermany
- Department of Orthopaedic and Trauma SurgeryMalteser Waldkrankenhaus ErlangenErlangenGermany
| | - Robert Feldt
- Department of Computer Science and EngineeringChalmers University of TechnologyGothenburgSweden
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14
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Barbieri F, Pfeifer BE, Senoner T, Dobner S, Spitaler P, Semsroth S, Lambert T, Zweiker D, Neururer SB, Scherr D, Schmidt A, Feuchtner GM, Hoppe UC, Adukauskaite A, Reinthaler M, Landmesser U, Müller S, Steinwender C, Dichtl W. A Neuronal Network-Based Score Predicting Survival in Patients Undergoing Aortic Valve Intervention: The ABC-AS Score. J Clin Med 2024; 13:3691. [PMID: 38999259 PMCID: PMC11242068 DOI: 10.3390/jcm13133691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2024] [Revised: 06/18/2024] [Accepted: 06/21/2024] [Indexed: 07/14/2024] Open
Abstract
Background: Despite being the most commonly performed valvular intervention, risk prediction for aortic valve replacement in patients with severe aortic stenosis by currently used risk scores remains challenging. The study aim was to develop a biomarker-based risk score by means of a neuronal network. Methods: In this multicenter study, 3595 patients were divided into test and validation cohorts (70% to 30%) by random allocation. Input variables to develop the ABC-AS score were age, the cardiac biomarker high-sensitivity troponin T, and a patient history of cardiac decompensation. The validation cohort was used to verify the scores' value and for comparison with the Society of Thoracic Surgery Predictive Risk of Operative Mortality score. Results: Receiver operating curves demonstrated an improvement in prediction by using the ABC-AS score compared to the Society of Thoracic Surgery Predictive Risk of Operative Mortality (STS prom) score. Although the difference in predicting cardiovascular mortality was most notable at 30-day follow-up (area under the curve of 0.922 versus 0.678), ABC-AS also performed better in overall follow-up (0.839 versus 0.699). Furthermore, univariate analysis of ABC-AS tertiles yielded highly significant differences for all-cause (p < 0.0001) and cardiovascular mortality (p < 0.0001). Head-to-head comparison between both risk scores in a multivariable cox regression model underlined the potential of the ABC-AS score (HR per z-unit 2.633 (95% CI 2.156-3.216), p < 0.0001), while the STS prom score failed to reach statistical significance (p = 0.226). Conclusions: The newly developed ABC-AS score is an improved risk stratification tool to predict cardiovascular outcomes for patients undergoing aortic valve intervention.
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Affiliation(s)
- Fabian Barbieri
- Department of Cardiology, Angiology and Intensive Care, Deutsches Herzzentrum der Charité, 12203 Berlin, Germany
- Department of Internal Medicine III, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Bernhard Erich Pfeifer
- Institute of Clinical Epidemiology, Tirol Kliniken, 6020 Innsbruck, Austria
- Division for Digital Medicine and Telehealth, University for Health Sciences, Medical Informatics and Technology (UMIT), 6060 Hall in Tirol, Austria
| | - Thomas Senoner
- Department of Internal Medicine III, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Stephan Dobner
- Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland
- Department of Cardiology and Intensive Care, Clinic Ottakring, 1160 Vienna, Austria
| | - Philipp Spitaler
- Department of Internal Medicine III, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Severin Semsroth
- University Clinic of Heart Surgery, Medical University of Innsbruck, Anichstrasse 35, 6020 Innsbruck, Austria
| | - Thomas Lambert
- Department of Cardiology, Kepler University Hospital, Medical Faculty, Johannes Kepler University Linz, 4021 Linz, Austria
| | - David Zweiker
- Department of Cardiology and Intensive Care, Clinic Ottakring, 1160 Vienna, Austria
- Department of Internal Medicine, Division of Cardiology, Medical University Graz, 8010 Graz, Austria
| | - Sabrina Barbara Neururer
- Institute of Clinical Epidemiology, Tirol Kliniken, 6020 Innsbruck, Austria
- Division for Digital Medicine and Telehealth, University for Health Sciences, Medical Informatics and Technology (UMIT), 6060 Hall in Tirol, Austria
| | - Daniel Scherr
- Department of Internal Medicine, Division of Cardiology, Medical University Graz, 8010 Graz, Austria
| | - Albrecht Schmidt
- Department of Internal Medicine, Division of Cardiology, Medical University Graz, 8010 Graz, Austria
| | - Gudrun Maria Feuchtner
- University Clinic of Radiology, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Uta Charlotte Hoppe
- University Clinic of Internal Medicine II, Paracelsus Medical University, 5020 Salzburg, Austria
| | - Agne Adukauskaite
- Department of Internal Medicine III, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Markus Reinthaler
- Department of Cardiology, Angiology and Intensive Care, Deutsches Herzzentrum der Charité, 12203 Berlin, Germany
- Institute of Active Polymers and Berlin-Brandenburg Center for Regenerative Therapies, Helmholtz-Zentrum Hereon, 14513 Teltow, Germany
| | - Ulf Landmesser
- Department of Cardiology, Angiology and Intensive Care, Deutsches Herzzentrum der Charité, 12203 Berlin, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, 10785 Berlin, Germany
- Berlin Institute of Health (BIH), 10178 Berlin, Germany
| | - Silvana Müller
- Department of Internal Medicine III, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Clemens Steinwender
- Department of Cardiology, Kepler University Hospital, Medical Faculty, Johannes Kepler University Linz, 4021 Linz, Austria
| | - Wolfgang Dichtl
- Department of Internal Medicine III, Medical University of Innsbruck, 6020 Innsbruck, Austria
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Ahangaran M, Zhu H, Li R, Yin L, Jang J, Chaudhry AP, Farrer LA, Au R, Kolachalama VB. DREAMER: a computational framework to evaluate readiness of datasets for machine learning. BMC Med Inform Decis Mak 2024; 24:152. [PMID: 38831432 PMCID: PMC11149315 DOI: 10.1186/s12911-024-02544-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Accepted: 05/20/2024] [Indexed: 06/05/2024] Open
Abstract
BACKGROUND Machine learning (ML) has emerged as the predominant computational paradigm for analyzing large-scale datasets across diverse domains. The assessment of dataset quality stands as a pivotal precursor to the successful deployment of ML models. In this study, we introduce DREAMER (Data REAdiness for MachinE learning Research), an algorithmic framework leveraging supervised and unsupervised machine learning techniques to autonomously evaluate the suitability of tabular datasets for ML model development. DREAMER is openly accessible as a tool on GitHub and Docker, facilitating its adoption and further refinement within the research community.. RESULTS The proposed model in this study was applied to three distinct tabular datasets, resulting in notable enhancements in their quality with respect to readiness for ML tasks, as assessed through established data quality metrics. Our findings demonstrate the efficacy of the framework in substantially augmenting the original dataset quality, achieved through the elimination of extraneous features and rows. This refinement yielded improved accuracy across both supervised and unsupervised learning methodologies. CONCLUSION Our software presents an automated framework for data readiness, aimed at enhancing the integrity of raw datasets to facilitate robust utilization within ML pipelines. Through our proposed framework, we streamline the original dataset, resulting in enhanced accuracy and efficiency within the associated ML algorithms.
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Affiliation(s)
- Meysam Ahangaran
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Hanzhi Zhu
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Ruihui Li
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Lingkai Yin
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Joseph Jang
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Arnav P Chaudhry
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Lindsay A Farrer
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department Ophthalmology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Boston University Alzheimer's Disease Research Center, Boston, MA, USA
- The Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Rhoda Au
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
- Boston University Alzheimer's Disease Research Center, Boston, MA, USA
- The Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Vijaya B Kolachalama
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
- Boston University Alzheimer's Disease Research Center, Boston, MA, USA.
- Department of Computer Science, Boston University, Boston, MA, USA.
- Faculty of Computing & Data Sciences, Boston University, Boston, MA, 02215, USA.
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16
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Lebech Cichosz S, Bender C. Development of Machine Learning Models for the Identification of Elevated Ketone Bodies During Hyperglycemia in Patients with Type 1 Diabetes. Diabetes Technol Ther 2024; 26:403-410. [PMID: 38456910 DOI: 10.1089/dia.2023.0531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/09/2024]
Abstract
Aims: Diabetic ketoacidosis (DKA) is a serious life-threatening condition caused by a lack of insulin, which leads to elevated plasma glucose and metabolic acidosis. Early identification of developing DKA is important to start treatment and minimize complications and risk of death. The aim of the present study is to develop and test prediction model(s) that gives an alarm about their risk of developing elevated ketone bodies during hyperglycemia. Methods: We analyzed data from 138 type 1 diabetes patients with measurements of ketone bodies and continuous glucose monitoring (CGM) data from over 30,000 days of wear time. We utilized a supervised binary classification machine learning approach to identify elevated levels of ketone bodies (≥0.6 mmol/L). Data material was randomly divided at patient level in 70%/30% (training/test) dataset. Logistic regression (LR) and random forest (RF) classifier were compared. Results: Among included patients, 913 ketone samples were eligible for modeling, including 273 event samples with ketone levels ≥0.6 mmol/L. An area under the receiver operating characteristic curve from the RF classifier was 0.836 (confidence interval [CI] 90%, 0.783-0.886) and 0.710 (CI 90%, 0.646-0.77) for the LR classifier. Conclusions: The novel approach for identifying elevated ketone levels in patients with type 1 diabetes utilized in this study indicates that CGM could be a valuable resource for the early prediction of patients at risk of developing DKA. Future studies are needed to validate the results.
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Affiliation(s)
- Simon Lebech Cichosz
- Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Clara Bender
- Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
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17
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Farah L, Borget I, Martelli N, Vallee A. Suitability of the Current Health Technology Assessment of Innovative Artificial Intelligence-Based Medical Devices: Scoping Literature Review. J Med Internet Res 2024; 26:e51514. [PMID: 38739911 PMCID: PMC11130781 DOI: 10.2196/51514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 12/17/2023] [Accepted: 12/28/2023] [Indexed: 05/16/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI)-based medical devices have garnered attention due to their ability to revolutionize medicine. Their health technology assessment framework is lacking. OBJECTIVE This study aims to analyze the suitability of each health technology assessment (HTA) domain for the assessment of AI-based medical devices. METHODS We conducted a scoping literature review following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology. We searched databases (PubMed, Embase, and Cochrane Library), gray literature, and HTA agency websites. RESULTS A total of 10.1% (78/775) of the references were included. Data quality and integration are vital aspects to consider when describing and assessing the technical characteristics of AI-based medical devices during an HTA process. When it comes to implementing specialized HTA for AI-based medical devices, several practical challenges and potential barriers could be highlighted and should be taken into account (AI technological evolution timeline, data requirements, complexity and transparency, clinical validation and safety requirements, regulatory and ethical considerations, and economic evaluation). CONCLUSIONS The adaptation of the HTA process through a methodological framework for AI-based medical devices enhances the comparability of results across different evaluations and jurisdictions. By defining the necessary expertise, the framework supports the development of a skilled workforce capable of conducting robust and reliable HTAs of AI-based medical devices. A comprehensive adapted HTA framework for AI-based medical devices can provide valuable insights into the effectiveness, cost-effectiveness, and societal impact of AI-based medical devices, guiding their responsible implementation and maximizing their benefits for patients and health care systems.
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Affiliation(s)
- Line Farah
- Innovation Center for Medical Devices Department, Foch Hospital, Suresnes, France
- Groupe de Recherche et d'accueil en Droit et Economie de la Santé Department, University Paris-Saclay, Orsay, France
| | - Isabelle Borget
- Groupe de Recherche et d'accueil en Droit et Economie de la Santé Department, University Paris-Saclay, Orsay, France
- Department of Biostatistics and Epidemiology, Gustave Roussy, University Paris-Saclay, Villejuif, France
- Oncostat U1018, Inserm, Équipe Labellisée Ligue Contre le Cancer, University Paris-Saclay, Villejuif, France
| | - Nicolas Martelli
- Groupe de Recherche et d'accueil en Droit et Economie de la Santé Department, University Paris-Saclay, Orsay, France
- Pharmacy Department, Georges Pompidou European Hospital, Paris, France
| | - Alexandre Vallee
- Department of Epidemiology and Public Health, Foch Hospital, Suresnes, France
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18
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Colangelo G, Ribo M, Montiel E, Dominguez D, Olivé-Gadea M, Muchada M, Garcia-Tornel Á, Requena M, Pagola J, Juega J, Rodriguez-Luna D, Rodriguez-Villatoro N, Rizzo F, Taborda B, Molina CA, Rubiera M. PRERISK: A Personalized, Artificial Intelligence-Based and Statistically-Based Stroke Recurrence Predictor for Recurrent Stroke. Stroke 2024; 55:1200-1209. [PMID: 38545798 DOI: 10.1161/strokeaha.123.043691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 01/31/2024] [Indexed: 04/24/2024]
Abstract
BACKGROUND Predicting stroke recurrence for individual patients is difficult, but individualized prediction may improve stroke survivors' engagement in self-care. We developed PRERISK: a statistical and machine learning classifier to predict individual risk of stroke recurrence. METHODS We analyzed clinical and socioeconomic data from a prospectively collected public health care-based data set of 41 975 patients admitted with stroke diagnosis in 88 public health centers over 6 years (2014-2020) in Catalonia-Spain. A new stroke diagnosis at least 24 hours after the index event was considered as a recurrent stroke, which was considered as our outcome of interest. We trained several supervised machine learning models to provide individualized risk over time and compared them with a Cox regression model. Models were trained to predict early, late, and long-term recurrence risk, within 90, 91 to 365, and >365 days, respectively. C statistics and area under the receiver operating characteristic curve were used to assess the accuracy of the models. RESULTS Overall, 16.21% (5932 of 36 114) of patients had stroke recurrence during a median follow-up of 2.69 years. The most powerful predictors of stroke recurrence were time from previous stroke, Barthel Index, atrial fibrillation, dyslipidemia, age, diabetes, and sex, which were used to create a simplified model with similar performance, together with modifiable vascular risk factors (glycemia, body mass index, high blood pressure, cholesterol, tobacco dependence, and alcohol abuse). The areas under the receiver operating characteristic curve were 0.76 (95% CI, 0.74-0.77), 0.60 (95% CI, 0.58-0.61), and 0.71 (95% CI, 0.69-0.72) for early, late, and long-term recurrence risk, respectively. The areas under the receiver operating characteristic curve of the Cox risk class probability were 0.73 (95% CI, 0.72-0.75), 0.59 (95% CI, 0.57-0.61), and 0.67 (95% CI, 0.66-0.70); machine learning approaches (random forest and AdaBoost) showed statistically significant improvement (P<0.05) over the Cox model for the 3 recurrence time periods. Stroke recurrence curves can be simulated for each patient under different degrees of control of modifiable factors. CONCLUSIONS PRERISK is a novel approach that provides a personalized and fairly accurate risk prediction of stroke recurrence over time. The model has the potential to incorporate dynamic control of risk factors.
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Affiliation(s)
- Giorgio Colangelo
- Vall d'Hebron Research Institute, Passeig de la Vall d'Hebron, Barcelona, Spain (G.C., M. Ribo, M.O.-G., M.M., Á.G.-T., M. Requena, J.P., J.J., D.R.-L., N.R.-V., F.R., B.T., C.A.M., M. Rubiera)
- Nora Health, Passeig de la Vall d'Hebron, Barcelona, Spain (G.C., E.M.)
| | - Marc Ribo
- Vall d'Hebron Research Institute, Passeig de la Vall d'Hebron, Barcelona, Spain (G.C., M. Ribo, M.O.-G., M.M., Á.G.-T., M. Requena, J.P., J.J., D.R.-L., N.R.-V., F.R., B.T., C.A.M., M. Rubiera)
- Hospital Universitari Vall d'Hebron, Stroke Unit, Neurology Department, Passeig de la Vall d'Hebron, Barcelona, Spain (M. Ribo, M.O.-G., M.M., Á.G.-T., M. Requena, J.P., J.J., D.R.-L., N.R.-V., F.R., B.T., C.A.M., M. Rubiera)
| | - Estefanía Montiel
- Nora Health, Passeig de la Vall d'Hebron, Barcelona, Spain (G.C., E.M.)
| | - Didier Dominguez
- Programa d'Analítica de Dades per a la Recerca i la Innovació en Salut, Agència de Qualitat i Avaluació Sanitàries de Catalunya, Departament de Salut, Generalitat de Catalunya, Carrer de Roc Boronat, Barcelona, Spain (D.D.)
| | - Marta Olivé-Gadea
- Vall d'Hebron Research Institute, Passeig de la Vall d'Hebron, Barcelona, Spain (G.C., M. Ribo, M.O.-G., M.M., Á.G.-T., M. Requena, J.P., J.J., D.R.-L., N.R.-V., F.R., B.T., C.A.M., M. Rubiera)
- Hospital Universitari Vall d'Hebron, Stroke Unit, Neurology Department, Passeig de la Vall d'Hebron, Barcelona, Spain (M. Ribo, M.O.-G., M.M., Á.G.-T., M. Requena, J.P., J.J., D.R.-L., N.R.-V., F.R., B.T., C.A.M., M. Rubiera)
| | - Marian Muchada
- Vall d'Hebron Research Institute, Passeig de la Vall d'Hebron, Barcelona, Spain (G.C., M. Ribo, M.O.-G., M.M., Á.G.-T., M. Requena, J.P., J.J., D.R.-L., N.R.-V., F.R., B.T., C.A.M., M. Rubiera)
- Hospital Universitari Vall d'Hebron, Stroke Unit, Neurology Department, Passeig de la Vall d'Hebron, Barcelona, Spain (M. Ribo, M.O.-G., M.M., Á.G.-T., M. Requena, J.P., J.J., D.R.-L., N.R.-V., F.R., B.T., C.A.M., M. Rubiera)
| | - Álvaro Garcia-Tornel
- Vall d'Hebron Research Institute, Passeig de la Vall d'Hebron, Barcelona, Spain (G.C., M. Ribo, M.O.-G., M.M., Á.G.-T., M. Requena, J.P., J.J., D.R.-L., N.R.-V., F.R., B.T., C.A.M., M. Rubiera)
- Hospital Universitari Vall d'Hebron, Stroke Unit, Neurology Department, Passeig de la Vall d'Hebron, Barcelona, Spain (M. Ribo, M.O.-G., M.M., Á.G.-T., M. Requena, J.P., J.J., D.R.-L., N.R.-V., F.R., B.T., C.A.M., M. Rubiera)
| | - Manuel Requena
- Vall d'Hebron Research Institute, Passeig de la Vall d'Hebron, Barcelona, Spain (G.C., M. Ribo, M.O.-G., M.M., Á.G.-T., M. Requena, J.P., J.J., D.R.-L., N.R.-V., F.R., B.T., C.A.M., M. Rubiera)
- Hospital Universitari Vall d'Hebron, Stroke Unit, Neurology Department, Passeig de la Vall d'Hebron, Barcelona, Spain (M. Ribo, M.O.-G., M.M., Á.G.-T., M. Requena, J.P., J.J., D.R.-L., N.R.-V., F.R., B.T., C.A.M., M. Rubiera)
| | - Jorge Pagola
- Vall d'Hebron Research Institute, Passeig de la Vall d'Hebron, Barcelona, Spain (G.C., M. Ribo, M.O.-G., M.M., Á.G.-T., M. Requena, J.P., J.J., D.R.-L., N.R.-V., F.R., B.T., C.A.M., M. Rubiera)
- Hospital Universitari Vall d'Hebron, Stroke Unit, Neurology Department, Passeig de la Vall d'Hebron, Barcelona, Spain (M. Ribo, M.O.-G., M.M., Á.G.-T., M. Requena, J.P., J.J., D.R.-L., N.R.-V., F.R., B.T., C.A.M., M. Rubiera)
| | - Jesús Juega
- Vall d'Hebron Research Institute, Passeig de la Vall d'Hebron, Barcelona, Spain (G.C., M. Ribo, M.O.-G., M.M., Á.G.-T., M. Requena, J.P., J.J., D.R.-L., N.R.-V., F.R., B.T., C.A.M., M. Rubiera)
- Hospital Universitari Vall d'Hebron, Stroke Unit, Neurology Department, Passeig de la Vall d'Hebron, Barcelona, Spain (M. Ribo, M.O.-G., M.M., Á.G.-T., M. Requena, J.P., J.J., D.R.-L., N.R.-V., F.R., B.T., C.A.M., M. Rubiera)
| | - David Rodriguez-Luna
- Vall d'Hebron Research Institute, Passeig de la Vall d'Hebron, Barcelona, Spain (G.C., M. Ribo, M.O.-G., M.M., Á.G.-T., M. Requena, J.P., J.J., D.R.-L., N.R.-V., F.R., B.T., C.A.M., M. Rubiera)
- Hospital Universitari Vall d'Hebron, Stroke Unit, Neurology Department, Passeig de la Vall d'Hebron, Barcelona, Spain (M. Ribo, M.O.-G., M.M., Á.G.-T., M. Requena, J.P., J.J., D.R.-L., N.R.-V., F.R., B.T., C.A.M., M. Rubiera)
| | - Noelia Rodriguez-Villatoro
- Vall d'Hebron Research Institute, Passeig de la Vall d'Hebron, Barcelona, Spain (G.C., M. Ribo, M.O.-G., M.M., Á.G.-T., M. Requena, J.P., J.J., D.R.-L., N.R.-V., F.R., B.T., C.A.M., M. Rubiera)
- Hospital Universitari Vall d'Hebron, Stroke Unit, Neurology Department, Passeig de la Vall d'Hebron, Barcelona, Spain (M. Ribo, M.O.-G., M.M., Á.G.-T., M. Requena, J.P., J.J., D.R.-L., N.R.-V., F.R., B.T., C.A.M., M. Rubiera)
| | - Federica Rizzo
- Vall d'Hebron Research Institute, Passeig de la Vall d'Hebron, Barcelona, Spain (G.C., M. Ribo, M.O.-G., M.M., Á.G.-T., M. Requena, J.P., J.J., D.R.-L., N.R.-V., F.R., B.T., C.A.M., M. Rubiera)
- Hospital Universitari Vall d'Hebron, Stroke Unit, Neurology Department, Passeig de la Vall d'Hebron, Barcelona, Spain (M. Ribo, M.O.-G., M.M., Á.G.-T., M. Requena, J.P., J.J., D.R.-L., N.R.-V., F.R., B.T., C.A.M., M. Rubiera)
| | - Belén Taborda
- Vall d'Hebron Research Institute, Passeig de la Vall d'Hebron, Barcelona, Spain (G.C., M. Ribo, M.O.-G., M.M., Á.G.-T., M. Requena, J.P., J.J., D.R.-L., N.R.-V., F.R., B.T., C.A.M., M. Rubiera)
- Hospital Universitari Vall d'Hebron, Stroke Unit, Neurology Department, Passeig de la Vall d'Hebron, Barcelona, Spain (M. Ribo, M.O.-G., M.M., Á.G.-T., M. Requena, J.P., J.J., D.R.-L., N.R.-V., F.R., B.T., C.A.M., M. Rubiera)
| | - Carlos A Molina
- Vall d'Hebron Research Institute, Passeig de la Vall d'Hebron, Barcelona, Spain (G.C., M. Ribo, M.O.-G., M.M., Á.G.-T., M. Requena, J.P., J.J., D.R.-L., N.R.-V., F.R., B.T., C.A.M., M. Rubiera)
- Hospital Universitari Vall d'Hebron, Stroke Unit, Neurology Department, Passeig de la Vall d'Hebron, Barcelona, Spain (M. Ribo, M.O.-G., M.M., Á.G.-T., M. Requena, J.P., J.J., D.R.-L., N.R.-V., F.R., B.T., C.A.M., M. Rubiera)
| | - Marta Rubiera
- Vall d'Hebron Research Institute, Passeig de la Vall d'Hebron, Barcelona, Spain (G.C., M. Ribo, M.O.-G., M.M., Á.G.-T., M. Requena, J.P., J.J., D.R.-L., N.R.-V., F.R., B.T., C.A.M., M. Rubiera)
- Hospital Universitari Vall d'Hebron, Stroke Unit, Neurology Department, Passeig de la Vall d'Hebron, Barcelona, Spain (M. Ribo, M.O.-G., M.M., Á.G.-T., M. Requena, J.P., J.J., D.R.-L., N.R.-V., F.R., B.T., C.A.M., M. Rubiera)
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19
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Khan SD, Hoodbhoy Z, Raja MHR, Kim JY, Hogg HDJ, Manji AAA, Gulamali F, Hasan A, Shaikh A, Tajuddin S, Khan NS, Patel MR, Balu S, Samad Z, Sendak MP. Frameworks for procurement, integration, monitoring, and evaluation of artificial intelligence tools in clinical settings: A systematic review. PLOS DIGITAL HEALTH 2024; 3:e0000514. [PMID: 38809946 PMCID: PMC11135672 DOI: 10.1371/journal.pdig.0000514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 04/18/2024] [Indexed: 05/31/2024]
Abstract
Research on the applications of artificial intelligence (AI) tools in medicine has increased exponentially over the last few years but its implementation in clinical practice has not seen a commensurate increase with a lack of consensus on implementing and maintaining such tools. This systematic review aims to summarize frameworks focusing on procuring, implementing, monitoring, and evaluating AI tools in clinical practice. A comprehensive literature search, following PRSIMA guidelines was performed on MEDLINE, Wiley Cochrane, Scopus, and EBSCO databases, to identify and include articles recommending practices, frameworks or guidelines for AI procurement, integration, monitoring, and evaluation. From the included articles, data regarding study aim, use of a framework, rationale of the framework, details regarding AI implementation involving procurement, integration, monitoring, and evaluation were extracted. The extracted details were then mapped on to the Donabedian Plan, Do, Study, Act cycle domains. The search yielded 17,537 unique articles, out of which 47 were evaluated for inclusion based on their full texts and 25 articles were included in the review. Common themes extracted included transparency, feasibility of operation within existing workflows, integrating into existing workflows, validation of the tool using predefined performance indicators and improving the algorithm and/or adjusting the tool to improve performance. Among the four domains (Plan, Do, Study, Act) the most common domain was Plan (84%, n = 21), followed by Study (60%, n = 15), Do (52%, n = 13), & Act (24%, n = 6). Among 172 authors, only 1 (0.6%) was from a low-income country (LIC) and 2 (1.2%) were from lower-middle-income countries (LMICs). Healthcare professionals cite the implementation of AI tools within clinical settings as challenging owing to low levels of evidence focusing on integration in the Do and Act domains. The current healthcare AI landscape calls for increased data sharing and knowledge translation to facilitate common goals and reap maximum clinical benefit.
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Affiliation(s)
- Sarim Dawar Khan
- CITRIC Health Data Science Centre, Department of Medicine, Aga Khan University, Karachi, Pakistan
| | - Zahra Hoodbhoy
- CITRIC Health Data Science Centre, Department of Medicine, Aga Khan University, Karachi, Pakistan
- Department of Paediatrics and Child Health, Aga Khan University, Karachi, Pakistan
| | | | - Jee Young Kim
- Duke Institute for Health Innovation, Duke University School of Medicine, Durham, North Carolina, United States
| | - Henry David Jeffry Hogg
- Population Health Science Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
- Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
| | - Afshan Anwar Ali Manji
- CITRIC Health Data Science Centre, Department of Medicine, Aga Khan University, Karachi, Pakistan
| | - Freya Gulamali
- Duke Institute for Health Innovation, Duke University School of Medicine, Durham, North Carolina, United States
| | - Alifia Hasan
- Duke Institute for Health Innovation, Duke University School of Medicine, Durham, North Carolina, United States
| | - Asim Shaikh
- CITRIC Health Data Science Centre, Department of Medicine, Aga Khan University, Karachi, Pakistan
| | - Salma Tajuddin
- CITRIC Health Data Science Centre, Department of Medicine, Aga Khan University, Karachi, Pakistan
| | - Nida Saddaf Khan
- CITRIC Health Data Science Centre, Department of Medicine, Aga Khan University, Karachi, Pakistan
| | - Manesh R. Patel
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina, United States
- Division of Cardiology, Duke University School of Medicine, Durham, North Carolina, United States
| | - Suresh Balu
- Duke Institute for Health Innovation, Duke University School of Medicine, Durham, North Carolina, United States
| | - Zainab Samad
- CITRIC Health Data Science Centre, Department of Medicine, Aga Khan University, Karachi, Pakistan
- Department of Medicine, Aga Khan University, Karachi, Pakistan
| | - Mark P. Sendak
- Duke Institute for Health Innovation, Duke University School of Medicine, Durham, North Carolina, United States
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20
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Biccirè FG, Mannhart D, Kakizaki R, Windecker S, Räber L, Siontis GCM. Automatic assessment of atherosclerotic plaque features by intracoronary imaging: a scoping review. Front Cardiovasc Med 2024; 11:1332925. [PMID: 38742173 PMCID: PMC11090039 DOI: 10.3389/fcvm.2024.1332925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 04/01/2024] [Indexed: 05/16/2024] Open
Abstract
Background The diagnostic performance and clinical validity of automatic intracoronary imaging (ICI) tools for atherosclerotic plaque assessment have not been systematically investigated so far. Methods We performed a scoping review including studies on automatic tools for automatic plaque components assessment by means of optical coherence tomography (OCT) or intravascular imaging (IVUS). We summarized study characteristics and reported the specifics and diagnostic performance of developed tools. Results Overall, 42 OCT and 26 IVUS studies fulfilling the eligibility criteria were found, with the majority published in the last 5 years (86% of the OCT and 73% of the IVUS studies). A convolutional neural network deep-learning method was applied in 71% of OCT- and 34% of IVUS-studies. Calcium was the most frequent plaque feature analyzed (26/42 of OCT and 12/26 of IVUS studies), and both modalities showed high discriminatory performance in testing sets [range of area under the curve (AUC): 0.91-0.99 for OCT and 0.89-0.98 for IVUS]. Lipid component was investigated only in OCT studies (n = 26, AUC: 0.82-0.86). Fibrous cap thickness or thin-cap fibroatheroma were mainly investigated in OCT studies (n = 8, AUC: 0.82-0.94). Plaque burden was mainly assessed in IVUS studies (n = 15, testing set AUC reported in one study: 0.70). Conclusion A limited number of automatic machine learning-derived tools for ICI analysis is currently available. The majority have been developed for calcium detection for either OCT or IVUS images. The reporting of the development and validation process of automated intracoronary imaging analyses is heterogeneous and lacks critical information. Systematic Review Registration Open Science Framework (OSF), https://osf.io/nps2b/.Graphical AbstractCentral Illustration.
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Affiliation(s)
| | | | | | | | | | - George C. M. Siontis
- Department of Cardiology, Bern University Hospital, University of Bern, Bern, Switzerland
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21
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Pannunzio V, Morales Ornelas HC, Gurung P, van Kooten R, Snelders D, van Os H, Wouters M, Tollenaar R, Atsma D, Kleinsmann M. Patient and Staff Experience of Remote Patient Monitoring-What to Measure and How: Systematic Review. J Med Internet Res 2024; 26:e48463. [PMID: 38648090 PMCID: PMC11074906 DOI: 10.2196/48463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 08/25/2023] [Accepted: 02/20/2024] [Indexed: 04/25/2024] Open
Abstract
BACKGROUND Patient and staff experience is a vital factor to consider in the evaluation of remote patient monitoring (RPM) interventions. However, no comprehensive overview of available RPM patient and staff experience-measuring methods and tools exists. OBJECTIVE This review aimed at obtaining a comprehensive set of experience constructs and corresponding measuring instruments used in contemporary RPM research and at proposing an initial set of guidelines for improving methodological standardization in this domain. METHODS Full-text papers reporting on instances of patient or staff experience measuring in RPM interventions, written in English, and published after January 1, 2011, were considered for eligibility. By "RPM interventions," we referred to interventions including sensor-based patient monitoring used for clinical decision-making; papers reporting on other kinds of interventions were therefore excluded. Papers describing primary care interventions, involving participants under 18 years of age, or focusing on attitudes or technologies rather than specific interventions were also excluded. We searched 2 electronic databases, Medline (PubMed) and EMBASE, on February 12, 2021.We explored and structured the obtained corpus of data through correspondence analysis, a multivariate statistical technique. RESULTS In total, 158 papers were included, covering RPM interventions in a variety of domains. From these studies, we reported 546 experience-measuring instances in RPM, covering the use of 160 unique experience-measuring instruments to measure 120 unique experience constructs. We found that the research landscape has seen a sizeable growth in the past decade, that it is affected by a relative lack of focus on the experience of staff, and that the overall corpus of collected experience measures can be organized in 4 main categories (service system related, care related, usage and adherence related, and health outcome related). In the light of the collected findings, we provided a set of 6 actionable recommendations to RPM patient and staff experience evaluators, in terms of both what to measure and how to measure it. Overall, we suggested that RPM researchers and practitioners include experience measuring as part of integrated, interdisciplinary data strategies for continuous RPM evaluation. CONCLUSIONS At present, there is a lack of consensus and standardization in the methods used to measure patient and staff experience in RPM, leading to a critical knowledge gap in our understanding of the impact of RPM interventions. This review offers targeted support for RPM experience evaluators by providing a structured, comprehensive overview of contemporary patient and staff experience measures and a set of practical guidelines for improving research quality and standardization in this domain.
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Affiliation(s)
- Valeria Pannunzio
- Department of Design, Organisation and Strategy, Faculty of Industrial Design Engineering, Delft University of Technology, Delft, Netherlands
| | - Hosana Cristina Morales Ornelas
- Department of Sustainable Design Engineering, Faculty of Industrial Design Engineering, Delft University of Technology, Delft, Netherlands
| | - Pema Gurung
- Walaeus Library, Leiden University Medical Center, Leiden, Netherlands
| | - Robert van Kooten
- Department of Surgery, Leiden University Medical Center, Leiden, Netherlands
| | - Dirk Snelders
- Department of Design, Organisation and Strategy, Faculty of Industrial Design Engineering, Delft University of Technology, Delft, Netherlands
| | - Hendrikus van Os
- National eHealth Living Lab, Department of Public Health & Primary Care, Leiden University Medical Center, Leiden, Netherlands
| | - Michel Wouters
- Department of Surgery, Netherlands Cancer Institute - Antoni van Leeuwenhoek, Amsterdam, Netherlands
| | - Rob Tollenaar
- Department of Surgery, Leiden University Medical Center, Leiden, Netherlands
| | - Douwe Atsma
- Department of Cardiology, Leiden University Medical Center, Leiden, Netherlands
| | - Maaike Kleinsmann
- Department of Design, Organisation and Strategy, Faculty of Industrial Design Engineering, Delft University of Technology, Delft, Netherlands
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22
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Collins GS, Moons KGM, Dhiman P, Riley RD, Beam AL, Van Calster B, Ghassemi M, Liu X, Reitsma JB, van Smeden M, Boulesteix AL, Camaradou JC, Celi LA, Denaxas S, Denniston AK, Glocker B, Golub RM, Harvey H, Heinze G, Hoffman MM, Kengne AP, Lam E, Lee N, Loder EW, Maier-Hein L, Mateen BA, McCradden MD, Oakden-Rayner L, Ordish J, Parnell R, Rose S, Singh K, Wynants L, Logullo P. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ 2024; 385:e078378. [PMID: 38626948 PMCID: PMC11019967 DOI: 10.1136/bmj-2023-078378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/17/2024] [Indexed: 04/19/2024]
Affiliation(s)
- Gary S Collins
- Centre for Statistics in Medicine, UK EQUATOR Centre, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
| | - Karel G M Moons
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Paula Dhiman
- Centre for Statistics in Medicine, UK EQUATOR Centre, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
| | - Richard D Riley
- Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
- National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, Birmingham, UK
| | - Andrew L Beam
- Department of Epidemiology, Harvard T H Chan School of Public Health, Boston, MA, USA
| | - Ben Van Calster
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Biomedical Data Science, Leiden University Medical Centre, Leiden, Netherlands
| | - Marzyeh Ghassemi
- Department of Electrical Engineering and Computer Science, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Xiaoxuan Liu
- Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Johannes B Reitsma
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Maarten van Smeden
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Anne-Laure Boulesteix
- Institute for Medical Information Processing, Biometry and Epidemiology, Faculty of Medicine, Ludwig-Maximilians-University of Munich and Munich Centre of Machine Learning, Germany
| | - Jennifer Catherine Camaradou
- Patient representative, Health Data Research UK patient and public involvement and engagement group
- Patient representative, University of East Anglia, Faculty of Health Sciences, Norwich Research Park, Norwich, UK
| | - Leo Anthony Celi
- Beth Israel Deaconess Medical Center, Boston, MA, USA
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Biostatistics, Harvard T H Chan School of Public Health, Boston, MA, USA
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK
- British Heart Foundation Data Science Centre, London, UK
| | - Alastair K Denniston
- National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, Birmingham, UK
- Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - Ben Glocker
- Department of Computing, Imperial College London, London, UK
| | - Robert M Golub
- Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | | | - Georg Heinze
- Section for Clinical Biometrics, Centre for Medical Data Science, Medical University of Vienna, Vienna, Austria
| | - Michael M Hoffman
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada
| | | | - Emily Lam
- Patient representative, Health Data Research UK patient and public involvement and engagement group
| | - Naomi Lee
- National Institute for Health and Care Excellence, London, UK
| | - Elizabeth W Loder
- The BMJ, London, UK
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Lena Maier-Hein
- Department of Intelligent Medical Systems, German Cancer Research Centre, Heidelberg, Germany
| | - Bilal A Mateen
- Institute of Health Informatics, University College London, London, UK
- Wellcome Trust, London, UK
- Alan Turing Institute, London, UK
| | - Melissa D McCradden
- Department of Bioethics, Hospital for Sick Children Toronto, ON, Canada
- Genetics and Genome Biology, SickKids Research Institute, Toronto, ON, Canada
| | - Lauren Oakden-Rayner
- Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia
| | - Johan Ordish
- Medicines and Healthcare products Regulatory Agency, London, UK
| | - Richard Parnell
- Patient representative, Health Data Research UK patient and public involvement and engagement group
| | - Sherri Rose
- Department of Health Policy and Center for Health Policy, Stanford University, Stanford, CA, USA
| | - Karandeep Singh
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Maastricht, Netherlands
| | - Laure Wynants
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Maastricht, Netherlands
| | - Patricia Logullo
- Centre for Statistics in Medicine, UK EQUATOR Centre, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
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Ranjbar A, Mork EW, Ravn J, Brøgger H, Myrseth P, Østrem HP, Hallock H. Managing Risk and Quality of AI in Healthcare: Are Hospitals Ready for Implementation? Risk Manag Healthc Policy 2024; 17:877-882. [PMID: 38617593 PMCID: PMC11016246 DOI: 10.2147/rmhp.s452337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 03/20/2024] [Indexed: 04/16/2024] Open
Abstract
Artificial intelligence (AI) provides a unique opportunity to help meet the demands of the future healthcare system. However, hospitals may not be well equipped to handle safe and effective development and/or procurement of AI systems. Furthermore, upcoming regulations such as the EU AI Act may enforce the need to establish new management systems, quality assurance and control mechanisms, novel to healthcare organizations. This paper discusses challenges in AI implementation, particularly potential gaps in current management systems (MS), by reviewing the harmonized standard for AI MS, ISO 42001, as part of a gap analysis of a tertiary acute hospital with ongoing AI activities. Examination of the industry agnostic ISO 42001 reveals a technical debt within healthcare, aligning with previous research on digitalization and AI implementation. To successfully implement AI with quality assurance in mind, emphasis should be put on the foundation and structure of the healthcare organizations, including both workforce and data infrastructure.
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Affiliation(s)
- Arian Ranjbar
- Medical Technology and E-Health, Akershus University Hospital, Lørenskog, Norway
| | | | - Jesper Ravn
- Medical Technology and E-Health, Akershus University Hospital, Lørenskog, Norway
| | - Helga Brøgger
- Group Research and Development, DNV AS, Høvik, Norway
| | - Per Myrseth
- Group Research and Development, DNV AS, Høvik, Norway
| | | | - Harry Hallock
- Group Research and Development, DNV AS, Høvik, Norway
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24
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Dang VN, Cascarano A, Mulder RH, Cecil C, Zuluaga MA, Hernández-González J, Lekadir K. Fairness and bias correction in machine learning for depression prediction across four study populations. Sci Rep 2024; 14:7848. [PMID: 38570587 PMCID: PMC10991528 DOI: 10.1038/s41598-024-58427-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 03/28/2024] [Indexed: 04/05/2024] Open
Abstract
A significant level of stigma and inequality exists in mental healthcare, especially in under-served populations. Inequalities are reflected in the data collected for scientific purposes. When not properly accounted for, machine learning (ML) models learned from data can reinforce these structural inequalities or biases. Here, we present a systematic study of bias in ML models designed to predict depression in four different case studies covering different countries and populations. We find that standard ML approaches regularly present biased behaviors. We also show that mitigation techniques, both standard and our own post-hoc method, can be effective in reducing the level of unfair bias. There is no one best ML model for depression prediction that provides equality of outcomes. This emphasizes the importance of analyzing fairness during model selection and transparent reporting about the impact of debiasing interventions. Finally, we also identify positive habits and open challenges that practitioners could follow to enhance fairness in their models.
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Affiliation(s)
- Vien Ngoc Dang
- Departament de Matemàtiques i Informàtica, Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain.
| | - Anna Cascarano
- Departament de Matemàtiques i Informàtica, Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Rosa H Mulder
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Charlotte Cecil
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus MC, Rotterdam,University Medical Center Rotterdam, Rotterdam, The Netherlands
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | | | | | - Karim Lekadir
- Departament de Matemàtiques i Informàtica, Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
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25
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Kryszan K, Wylęgała A, Kijonka M, Potrawa P, Walasz M, Wylęgała E, Orzechowska-Wylęgała B. Artificial-Intelligence-Enhanced Analysis of In Vivo Confocal Microscopy in Corneal Diseases: A Review. Diagnostics (Basel) 2024; 14:694. [PMID: 38611606 PMCID: PMC11011861 DOI: 10.3390/diagnostics14070694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 03/13/2024] [Accepted: 03/22/2024] [Indexed: 04/14/2024] Open
Abstract
Artificial intelligence (AI) has seen significant progress in medical diagnostics, particularly in image and video analysis. This review focuses on the application of AI in analyzing in vivo confocal microscopy (IVCM) images for corneal diseases. The cornea, as an exposed and delicate part of the body, necessitates the precise diagnoses of various conditions. Convolutional neural networks (CNNs), a key component of deep learning, are a powerful tool for image data analysis. This review highlights AI applications in diagnosing keratitis, dry eye disease, and diabetic corneal neuropathy. It discusses the potential of AI in detecting infectious agents, analyzing corneal nerve morphology, and identifying the subtle changes in nerve fiber characteristics in diabetic corneal neuropathy. However, challenges still remain, including limited datasets, overfitting, low-quality images, and unrepresentative training datasets. This review explores augmentation techniques and the importance of feature engineering to address these challenges. Despite the progress made, challenges are still present, such as the "black-box" nature of AI models and the need for explainable AI (XAI). Expanding datasets, fostering collaborative efforts, and developing user-friendly AI tools are crucial for enhancing the acceptance and integration of AI into clinical practice.
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Affiliation(s)
- Katarzyna Kryszan
- Chair and Clinical Department of Ophthalmology, School of Medicine in Zabrze, Medical University of Silesia in Katowice, District Railway Hospital, 40-760 Katowice, Poland; (A.W.); (M.K.); (E.W.)
- Department of Ophthalmology, District Railway Hospital in Katowice, 40-760 Katowice, Poland; (P.P.); (M.W.)
| | - Adam Wylęgała
- Chair and Clinical Department of Ophthalmology, School of Medicine in Zabrze, Medical University of Silesia in Katowice, District Railway Hospital, 40-760 Katowice, Poland; (A.W.); (M.K.); (E.W.)
- Health Promotion and Obesity Management, Pathophysiology Department, Medical University of Silesia in Katowice, 40-752 Katowice, Poland
| | - Magdalena Kijonka
- Chair and Clinical Department of Ophthalmology, School of Medicine in Zabrze, Medical University of Silesia in Katowice, District Railway Hospital, 40-760 Katowice, Poland; (A.W.); (M.K.); (E.W.)
- Department of Ophthalmology, District Railway Hospital in Katowice, 40-760 Katowice, Poland; (P.P.); (M.W.)
| | - Patrycja Potrawa
- Department of Ophthalmology, District Railway Hospital in Katowice, 40-760 Katowice, Poland; (P.P.); (M.W.)
| | - Mateusz Walasz
- Department of Ophthalmology, District Railway Hospital in Katowice, 40-760 Katowice, Poland; (P.P.); (M.W.)
| | - Edward Wylęgała
- Chair and Clinical Department of Ophthalmology, School of Medicine in Zabrze, Medical University of Silesia in Katowice, District Railway Hospital, 40-760 Katowice, Poland; (A.W.); (M.K.); (E.W.)
- Department of Ophthalmology, District Railway Hospital in Katowice, 40-760 Katowice, Poland; (P.P.); (M.W.)
| | - Bogusława Orzechowska-Wylęgała
- Department of Pediatric Otolaryngology, Head and Neck Surgery, Chair of Pediatric Surgery, Medical University of Silesia, 40-760 Katowice, Poland;
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26
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Truhn D, Eckardt JN, Ferber D, Kather JN. Large language models and multimodal foundation models for precision oncology. NPJ Precis Oncol 2024; 8:72. [PMID: 38519519 PMCID: PMC10959931 DOI: 10.1038/s41698-024-00573-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 03/12/2024] [Indexed: 03/25/2024] Open
Abstract
The technological progress in artificial intelligence (AI) has massively accelerated since 2022, with far-reaching implications for oncology and cancer research. Large language models (LLMs) now perform at human-level competency in text processing. Notably, both text and image processing networks are increasingly based on transformer neural networks. This convergence enables the development of multimodal AI models that take diverse types of data as an input simultaneously, marking a qualitative shift from specialized niche models which were prevalent in the 2010s. This editorial summarizes these developments, which are expected to impact precision oncology in the coming years.
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Affiliation(s)
- Daniel Truhn
- Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany
| | - Jan-Niklas Eckardt
- Department of Internal Medicine I, University Hospital Carl Gustav Carus, Technical University Dresden, Dresden, Germany
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
| | - Dyke Ferber
- National Center for Tumor Diseases (NCT), Heidelberg University Hospital, Heidelberg, Germany
- Department of Medical Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Jakob Nikolas Kather
- Department of Internal Medicine I, University Hospital Carl Gustav Carus, Technical University Dresden, Dresden, Germany.
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany.
- National Center for Tumor Diseases (NCT), Heidelberg University Hospital, Heidelberg, Germany.
- Department of Medical Oncology, Heidelberg University Hospital, Heidelberg, Germany.
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27
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Hahn P. Artificial Intelligence and "Decision Intelligence". DEUTSCHES ARZTEBLATT INTERNATIONAL 2024; 121:205. [PMID: 38666682 PMCID: PMC11079805 DOI: 10.3238/arztebl.m2023.0213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/01/2024]
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28
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Duncan MD. An AI-driven risk assessment tool for predicting the severity of illness in acute pancreatitis. Proc AMIA Symp 2024; 37:448-449. [PMID: 38628338 PMCID: PMC11018033 DOI: 10.1080/08998280.2024.2328998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Accepted: 03/06/2024] [Indexed: 04/19/2024] Open
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Yang M, Chen H, Hu W, Mischi M, Shan C, Li J, Long X, Liu C. Development and Validation of an Interpretable Conformal Predictor to Predict Sepsis Mortality Risk: Retrospective Cohort Study. J Med Internet Res 2024; 26:e50369. [PMID: 38498038 PMCID: PMC10985608 DOI: 10.2196/50369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 10/16/2023] [Accepted: 01/24/2024] [Indexed: 03/19/2024] Open
Abstract
BACKGROUND Early and reliable identification of patients with sepsis who are at high risk of mortality is important to improve clinical outcomes. However, 3 major barriers to artificial intelligence (AI) models, including the lack of interpretability, the difficulty in generalizability, and the risk of automation bias, hinder the widespread adoption of AI models for use in clinical practice. OBJECTIVE This study aimed to develop and validate (internally and externally) a conformal predictor of sepsis mortality risk in patients who are critically ill, leveraging AI-assisted prediction modeling. The proposed approach enables explaining the model output and assessing its confidence level. METHODS We retrospectively extracted data on adult patients with sepsis from a database collected in a teaching hospital at Beth Israel Deaconess Medical Center for model training and internal validation. A large multicenter critical care database from the Philips eICU Research Institute was used for external validation. A total of 103 clinical features were extracted from the first day after admission. We developed an AI model using gradient-boosting machines to predict the mortality risk of sepsis and used Mondrian conformal prediction to estimate the prediction uncertainty. The Shapley additive explanation method was used to explain the model. RESULTS A total of 16,746 (80%) patients from Beth Israel Deaconess Medical Center were used to train the model. When tested on the internal validation population of 4187 (20%) patients, the model achieved an area under the receiver operating characteristic curve of 0.858 (95% CI 0.845-0.871), which was reduced to 0.800 (95% CI 0.789-0.811) when externally validated on 10,362 patients from the Philips eICU database. At a specified confidence level of 90% for the internal validation cohort the percentage of error predictions (n=438) out of all predictions (n=4187) was 10.5%, with 1229 (29.4%) predictions requiring clinician review. In contrast, the AI model without conformal prediction made 1449 (34.6%) errors. When externally validated, more predictions (n=4004, 38.6%) were flagged for clinician review due to interdatabase heterogeneity. Nevertheless, the model still produced significantly lower error rates compared to the point predictions by AI (n=1221, 11.8% vs n=4540, 43.8%). The most important predictors identified in this predictive model were Acute Physiology Score III, age, urine output, vasopressors, and pulmonary infection. Clinically relevant risk factors contributing to a single patient were also examined to show how the risk arose. CONCLUSIONS By combining model explanation and conformal prediction, AI-based systems can be better translated into medical practice for clinical decision-making.
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Affiliation(s)
- Meicheng Yang
- State Key Laboratory of Digital Medical Engineering, School of Instrument Science and Engineering, Southeast University, Nanjing, China
| | - Hui Chen
- Department of Critical Care Medicine, Jiangsu Provincial Key Laboratory of Critical Care Medicine, Zhongda Hospital, Southeast University, Nanjing, China
| | - Wenhan Hu
- Department of Critical Care Medicine, Jiangsu Provincial Key Laboratory of Critical Care Medicine, Zhongda Hospital, Southeast University, Nanjing, China
| | - Massimo Mischi
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Caifeng Shan
- College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, China
- School of Intelligence Science and Technology, Nanjing University, Nanjing, China
| | - Jianqing Li
- State Key Laboratory of Digital Medical Engineering, School of Instrument Science and Engineering, Southeast University, Nanjing, China
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Xi Long
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Chengyu Liu
- State Key Laboratory of Digital Medical Engineering, School of Instrument Science and Engineering, Southeast University, Nanjing, China
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30
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Grotenhuis Z, Mosteiro PJ, Leeuwenberg AM. Modest performance of text mining to extract health outcomes may be almost sufficient for high-quality prognostic model development. Comput Biol Med 2024; 170:108014. [PMID: 38301515 DOI: 10.1016/j.compbiomed.2024.108014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 01/03/2024] [Accepted: 01/19/2024] [Indexed: 02/03/2024]
Abstract
BACKGROUND Across medicine, prognostic models are used to estimate patient risk of certain future health outcomes (e.g., cardiovascular or mortality risk). To develop (or train) prognostic models, historic patient-level training data is needed containing both the predictive factors (i.e., features) and the relevant health outcomes (i.e., labels). Sometimes, when the health outcomes are not recorded in structured data, these are first extracted from textual notes using text mining techniques. Because there exist many studies utilizing text mining to obtain outcome data for prognostic model development, our aim is to study the impact of the text mining quality on downstream prognostic model performance. METHODS We conducted a simulation study charting the relationship between text mining quality and prognostic model performance using an illustrative case study about in-hospital mortality prediction in intensive care unit patients. We repeatedly developed and evaluated a prognostic model for in-hospital mortality, using outcome data extracted by multiple text mining models of varying quality. RESULTS Interestingly, we found in our case study that a relatively low-quality text mining model (F1 score ≈ 0.50) could already be used to train a prognostic model with quite good discrimination (area under the receiver operating characteristic curve of around 0.80). The calibration of the risks estimated by the prognostic model seemed unreliable across the majority of settings, even when text mining models were of relatively high quality (F1 ≈ 0.80). DISCUSSION Developing prognostic models on text-extracted outcomes using imperfect text mining models seems promising. However, it is likely that prognostic models developed using this approach may not produce well-calibrated risk estimates, and require recalibration in (possibly a smaller amount of) manually extracted outcome data.
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Affiliation(s)
- Zwierd Grotenhuis
- Department of Information and Computing Sciences, Utrecht University, The Netherlands; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, The Netherlands
| | - Pablo J Mosteiro
- Department of Information and Computing Sciences, Utrecht University, The Netherlands
| | - Artuur M Leeuwenberg
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, The Netherlands.
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31
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Evans RP, Bryant LD, Russell G, Absolom K. Trust and acceptability of data-driven clinical recommendations in everyday practice: A scoping review. Int J Med Inform 2024; 183:105342. [PMID: 38266426 DOI: 10.1016/j.ijmedinf.2024.105342] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 12/08/2023] [Accepted: 01/14/2024] [Indexed: 01/26/2024]
Abstract
BACKGROUND Increasing attention is being given to the analysis of large health datasets to derive new clinical decision support systems (CDSS). However, few data-driven CDSS are being adopted into clinical practice. Trust in these tools is believed to be fundamental for acceptance and uptake but to date little attention has been given to defining or evaluating trust in clinical settings. OBJECTIVES A scoping review was conducted to explore how and where acceptability and trustworthiness of data-driven CDSS have been assessed from the health professional's perspective. METHODS Medline, Embase, PsycInfo, Web of Science, Scopus, ACM Digital, IEEE Xplore and Google Scholar were searched in March 2022 using terms expanded from: "data-driven" AND "clinical decision support" AND "acceptability". Included studies focused on healthcare practitioner-facing data-driven CDSS, relating directly to clinical care. They included trust or a proxy as an outcome, or in the discussion. The preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews (PRISMA-ScR) is followed in the reporting of this review. RESULTS 3291 papers were screened, with 85 primary research studies eligible for inclusion. Studies covered a diverse range of clinical specialisms and intended contexts, but hypothetical systems (24) outnumbered those in clinical use (18). Twenty-five studies measured trust, via a wide variety of quantitative, qualitative and mixed methods. A further 24 discussed themes of trust without it being explicitly evaluated, and from these, themes of transparency, explainability, and supporting evidence were identified as factors influencing healthcare practitioner trust in data-driven CDSS. CONCLUSION There is a growing body of research on data-driven CDSS, but few studies have explored stakeholder perceptions in depth, with limited focused research on trustworthiness. Further research on healthcare practitioner acceptance, including requirements for transparency and explainability, should inform clinical implementation.
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Affiliation(s)
- Ruth P Evans
- University of Leeds, Woodhouse Lane, Leeds LS2 9JT, UK.
| | | | - Gregor Russell
- Bradford District Care Trust, Bradford, New Mill, Victoria Rd, BD18 3LD, UK.
| | - Kate Absolom
- University of Leeds, Woodhouse Lane, Leeds LS2 9JT, UK.
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32
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Wang P, Leong QY, Lau NY, Ng WY, Kwek SP, Tan L, Song SW, You K, Chong LM, Zhuang I, Ong YH, Foo N, Tadeo X, Kumar KS, Vijayakumar S, Sapanel Y, Raczkowska MN, Remus A, Blasiak A, Ho D. N-of-1 medicine. Singapore Med J 2024; 65:167-175. [PMID: 38527301 PMCID: PMC11060644 DOI: 10.4103/singaporemedj.smj-2023-243] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 01/19/2024] [Indexed: 03/27/2024]
Abstract
ABSTRACT The fields of precision and personalised medicine have led to promising advances in tailoring treatment to individual patients. Examples include genome/molecular alteration-guided drug selection, single-patient gene therapy design and synergy-based drug combination development, and these approaches can yield substantially diverse recommendations. Therefore, it is important to define each domain and delineate their commonalities and differences in an effort to develop novel clinical trial designs, streamline workflow development, rethink regulatory considerations, create value in healthcare and economics assessments, and other factors. These and other segments are essential to recognise the diversity within these domains to accelerate their respective workflows towards practice-changing healthcare. To emphasise these points, this article elaborates on the concept of digital health and digital medicine-enabled N-of-1 medicine, which individualises combination regimen and dosing using a patient's own data. We will conclude with recommendations for consideration when developing novel workflows based on emerging digital-based platforms.
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Affiliation(s)
- Peter Wang
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore
| | - Qiao Ying Leong
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
| | - Ni Yin Lau
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
| | - Wei Ying Ng
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
| | - Siong Peng Kwek
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
| | - Lester Tan
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore
| | - Shang-Wei Song
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore
| | - Kui You
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore
| | - Li Ming Chong
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore
| | - Isaiah Zhuang
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
| | - Yoong Hun Ong
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
| | - Nigel Foo
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore
| | - Xavier Tadeo
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
| | - Kirthika Senthil Kumar
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
| | - Smrithi Vijayakumar
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
| | - Yoann Sapanel
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
- Singapore’s Health District @ Queenstown, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Marlena Natalia Raczkowska
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
| | - Alexandria Remus
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore
- Heat Resilience Performance Centre (HRPC), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Agata Blasiak
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Dean Ho
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore
- Singapore’s Health District @ Queenstown, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The Bia-Echo Asia Centre for Reproductive Longevity and Equality, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
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Zhao L, Lin D, Lin H. Automated Machine Learning for Diabetic Retinopathy Progression. JAMA Ophthalmol 2024; 142:178-179. [PMID: 38329739 DOI: 10.1001/jamaophthalmol.2023.6778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Affiliation(s)
- Lanqin Zhao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Duoru Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Haotian Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Precision Medicine and Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
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Adeoye J, Su YX. Leveraging artificial intelligence for perioperative cancer risk assessment of oral potentially malignant disorders. Int J Surg 2024; 110:1677-1686. [PMID: 38051932 PMCID: PMC10942172 DOI: 10.1097/js9.0000000000000979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Accepted: 11/21/2023] [Indexed: 12/07/2023]
Abstract
Oral potentially malignant disorders (OPMDs) are mucosal conditions with an inherent disposition to develop oral squamous cell carcinoma. Surgical management is the most preferred strategy to prevent malignant transformation in OPMDs, and surgical approaches to treatment include conventional scalpel excision, laser surgery, cryotherapy, and photodynamic therapy. However, in reality, since all patients with OPMDs will not develop oral squamous cell carcinoma in their lifetime, there is a need to stratify patients according to their risk of malignant transformation to streamline surgical intervention for patients with the highest risks. Artificial intelligence (AI) has the potential to integrate disparate factors influencing malignant transformation for robust, precise, and personalized cancer risk stratification of OPMD patients than current methods to determine the need for surgical resection, excision, or re-excision. Therefore, this article overviews existing AI models and tools, presents a clinical implementation pathway, and discusses necessary refinements to aid the clinical application of AI-based platforms for cancer risk stratification of OPMDs in surgical practice.
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Affiliation(s)
| | - Yu-Xiong Su
- Division of Oral and Maxillofacial Surgery, Faculty of Dentistry, University of Hong Kong, Hong Kong SAR, People’s Republic of China
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Executive summary of the meeting of the 2023 ASHP Commission on Goals: Optimizing Medication Therapy Through Advanced Analytics and Data-Driven Healthcare. Am J Health Syst Pharm 2024; 81:159-164. [PMID: 37971063 DOI: 10.1093/ajhp/zxad261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2023] Open
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Moses JC, Adibi S, Angelova M, Islam SMS. Time-domain heart rate variability features for automatic congestive heart failure prediction. ESC Heart Fail 2024; 11:378-389. [PMID: 38009405 PMCID: PMC10804149 DOI: 10.1002/ehf2.14593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 10/10/2023] [Accepted: 11/07/2023] [Indexed: 11/28/2023] Open
Abstract
AIMS Heart failure is a serious condition that often goes undiagnosed in primary care due to the lack of reliable diagnostic tools and the similarity of its symptoms with other diseases. Non-invasive monitoring of heart rate variability (HRV), which reflects the activity of the autonomic nervous system, could offer a novel and accurate way to detect and manage heart failure patients. This study aimed to assess the feasibility of using machine learning techniques on HRV data as a non-invasive biomarker to classify healthy adults and those with heart failure. METHODS AND RESULTS We used digitized electrocardiogram recordings from 54 adults with normal sinus rhythm and 44 adults categorized into New York Heart Association classes 1, 2, and 3, suffering from congestive heart failure. All recordings were sourced from the PhysioNet database. Following data pre-processing, we performed time-domain HRV analysis on all individual recordings, including root mean square of the successive difference in adjacent RR interval (RRi) (RMSSD), the standard deviation of RRi (SDNN, the NN stands for natural or sinus intervals), the standard deviation of the successive differences between successive RRi (SDSD), the number or percentage of RRi longer than 50 ms (NN50 and pNN50), and the average value of RRi [mean RR interval (mRRi)]. In our experimental classification performance evaluation, on the computed HRV parameters, we optimized hyperparameters and performed five-fold cross-validation using four machine learning classification algorithms: support vector machine, k-nearest neighbour (KNN), naïve Bayes, and decision tree (DT). We evaluated the prediction accuracy of these models using performance criteria, namely, precision, recall, specificity, F1 score, and overall accuracy. For added insight, we also presented receiver operating characteristic (ROC) plots and area under the ROC curve (AUC) values. The overall best performance accuracy of 77% was achieved when KNN and DT were trained on computed HRV parameters with a 5 min time window. KNN obtained an AUC of 0.77, while DT attained 0.78. Additionally, in the classification of severe congestive heart failure, KNN and DT had the best accuracy of 91%, with KNN achieving an AUC of 0.88 and DT obtaining 0.92. CONCLUSIONS The results show that HRV can accurately predict severe congestive heart failure. The findings of this study could inform the use of machine learning approaches on non-invasive HRV, to screen congestive heart failure individuals in primary care.
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Affiliation(s)
| | - Sasan Adibi
- School of Information TechnologyDeakin UniversityBurwoodVIC3125Australia
| | - Maia Angelova
- School of Information TechnologyDeakin UniversityBurwoodVIC3125Australia
- Aston Digital Futures Institute, College of Physical Sciences and EngineeringAston UniversityBirminghamUK
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Peng Y, Wang Y, Wen Z, Xiang H, Guo L, Su L, He Y, Pang H, Zhou P, Zhan X. Deep learning and machine learning predictive models for neurological function after interventional embolization of intracranial aneurysms. Front Neurol 2024; 15:1321923. [PMID: 38327618 PMCID: PMC10848172 DOI: 10.3389/fneur.2024.1321923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 01/08/2024] [Indexed: 02/09/2024] Open
Abstract
Objective The objective of this study is to develop a model to predicts the postoperative Hunt-Hess grade in patients with intracranial aneurysms by integrating radiomics and deep learning technologies, using preoperative CTA imaging data. Thereby assisting clinical decision-making and improving the assessment and prognosis of postoperative neurological function. Methods This retrospective study encompassed 101 patients who underwent aneurysm embolization surgery. 851 radiomic features were extracted from CTA images. 512 deep learning features are extracted from last layer of ResNet50 deep convolutional neural network model. The feature screening process pipeline encompassed intraclass correlation coefficient analysis, principal component analysis, U test, spearman correlation analysis, minimum redundancy maximum relevance algorithm and Lasso regression, to identify features most correlated with postoperative Hunt-Hess grading. In the model construction phase, three distinct models were constructed: radiomics feature-based model (RSM), deep learning feature-based model (DLM), and deep learning-radiomics feature fusion model (DLRSCM). The study also calculated the radiomics score and combined it with clinical data to construct a Nomogram for predictive modeling. DLM, RSM and DLRSCM model was constructed by 9 base algorithms and 1 ensemble learning algorithm - Stacking ensemble model. Model performance was evaluated based on the area under the Receiver Operating Characteristic (ROC) curve (AUC), Matthews Correlation Coefficient (MCC), calibration curves, and decision curves analysis. Results 5 significant radiomic feature and 4 significant deep learning features were obtained through the feature selection process. These features were utilized for model construction. Bootstrap resampling method was used for internal validation of the models. In terms of model evaluation, the DLM model, the stacking ensemble algorithm results achieved an AUC of 0.959 and MCC of 0.815. In the RSM model, the stacking ensemble model AUC was 0.935 and MCC was 0.793. The stacking ensemble model in DLRSCM outperformed others, with an AUC of 0.968 and MCC of 0.820. Results indicated that the ANN performed optimally among all base models, while the stacked ensemble learning model exhibited the highest predictive performance. Conclusion This study demonstrates that the combination of radiomics and deep learning is an effective approach to predict the postoperative Hunt-Hess grade in patients with intracranial aneurysms. This holds significant value in the early identification of postoperative neurological complications and in enhancing clinical decision-making.
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Affiliation(s)
- Yan Peng
- Department of Interventional Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Yiren Wang
- School of Nursing, Southwest Medical University, Luzhou, China
- Wound Healing Basic Research and Clinical Application Key Laboratory of Luzhou, Southwest Medical University, Luzhou, China
| | - Zhongjian Wen
- School of Nursing, Southwest Medical University, Luzhou, China
- Wound Healing Basic Research and Clinical Application Key Laboratory of Luzhou, Southwest Medical University, Luzhou, China
| | - Hongli Xiang
- School of Nursing, Southwest Medical University, Luzhou, China
- Wound Healing Basic Research and Clinical Application Key Laboratory of Luzhou, Southwest Medical University, Luzhou, China
| | - Ling Guo
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Lei Su
- School of Medical Information and Engineering, Southwest Medical University, Luzhou, China
| | - Yongcheng He
- Department of Pharmacy, Sichuan Agriculture University, Chengdu, China
| | - Haowen Pang
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Ping Zhou
- Wound Healing Basic Research and Clinical Application Key Laboratory of Luzhou, Southwest Medical University, Luzhou, China
- Department of Nursing, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Xiang Zhan
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
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Valdivieso Shephard JL, Alvarez Robles EJ, Cámara Hijón C, Hernandez Breijo B, Novella-Navarro M, Bogas Schay P, Cuesta de la Cámara R, Balsa Criado A, López Granados E, Plasencia Rodríguez C. Predicting anti-TNF treatment response in rheumatoid arthritis: An artificial intelligence-driven model using cytokine profile and routine clinical practice parameters. Heliyon 2024; 10:e22925. [PMID: 38163219 PMCID: PMC10754867 DOI: 10.1016/j.heliyon.2023.e22925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 11/18/2023] [Accepted: 11/22/2023] [Indexed: 01/03/2024] Open
Abstract
Introduction Rheumatoid arthritis (RA) is a heterogeneous disease in which therapeutic strategies used have evolved dramatically. Despite significant progress in treatment strategies such as the development of anti-TNF drugs, it is still not possible to differentiate those patients who will respond from who will not. This can lead to effective-treatment delays and unnecessary costs. The aim of this study was to utilize a profile of the patient's characteristics, clinical parameters, immune status (cytokine profile) and artificial intelligence to assess the feasibility of developing a tool that could allow us to predict which patients will respond to treatment with anti-TNF drugs. Methods This study included 38 patients with RA from the RA-Paz cohort. Clinical activity was measured at baseline and after 6 months of treatment. The cytokines measured before the start of anti-TNF treatment were IL-1, IL-12, IL-10, IL-2, IL-4, IFNg, TNFa, and IL-6. Statistical analyses were performed using the Wilcoxon-Rank-Sum Test and the Benjamini-Hochberg method. The predictive model viability was explored using the 5-fold cross-validation scheme in order to train the logistic regression models. Results Statistically significant differences were found in parameters such as IL-6, IL-2, CRP and DAS-ESR. The predictive model performed to an acceptable level in correctly classifying patients (ROC-AUC 0.804167 to 0.891667), suggesting that it would be possible to develop a clinical classification tool. Conclusions Using a combination of parameters such as IL-6, IL-2, CRP and DAS-ESR, it was possible to develop a predictive model that can acceptably discriminate between remitters and non-remitters. However, this model needs to be replicated in a larger cohort to confirm these findings.
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Affiliation(s)
| | | | | | - Borja Hernandez Breijo
- Immunology-Rheumatology Research Group, Hospital Universitario La Paz-Idipaz, Madrid, Spain
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Saccà R, Turrini R, Ausania F, Turrina S, De Leo D. The ménage à trois of healthcare: the actors in after-AI era under patient consent. Front Med (Lausanne) 2024; 10:1329087. [PMID: 38269319 PMCID: PMC10807288 DOI: 10.3389/fmed.2023.1329087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 12/27/2023] [Indexed: 01/26/2024] Open
Abstract
Introduction Artificial intelligence has become an increasingly powerful technological instrument in recent years, revolutionizing many sectors, including public health. Its use in this field will inevitably change clinical practice, the patient-caregiver relationship and the concept of the diagnosis and treatment pathway, affecting the balance between the patient's right to self-determination and health, and thus leading to an evolution of the concept of informed consent. The aim was to characterize the guidelines for the use of artificial intelligence, its areas of application and the relevant legislation, to propose guiding principles for the design of optimal informed consent for its use. Materials and methods A classic review by keywords on the main search engines was conducted. An analysis of the guidelines and regulations issued by scientific authorities and legal bodies on the use of artificial intelligence in public health was carried out. Results The current areas of application of this technology were highlighted, divided into sectors, its impact on them, as well as a summary of current guidelines and legislation. Discussion The ethical implications of artificial intelligence in the health care system were assessed, particularly regarding the therapeutic alliance between doctor and patient, and the balance between the right to self-determination and health. Finally, given the evolution of informed consent in relation to the use of this new technology, seven guiding principles were proposed to guarantee the right to the most informed consent or dissent.
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Gallifant J, Fiske A, Levites Strekalova YA, Osorio-Valencia JS, Parke R, Mwavu R, Martinez N, Gichoya JW, Ghassemi M, Demner-Fushman D, McCoy LG, Celi LA, Pierce R. Peer review of GPT-4 technical report and systems card. PLOS DIGITAL HEALTH 2024; 3:e0000417. [PMID: 38236824 PMCID: PMC10795998 DOI: 10.1371/journal.pdig.0000417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/22/2024]
Abstract
The study provides a comprehensive review of OpenAI's Generative Pre-trained Transformer 4 (GPT-4) technical report, with an emphasis on applications in high-risk settings like healthcare. A diverse team, including experts in artificial intelligence (AI), natural language processing, public health, law, policy, social science, healthcare research, and bioethics, analyzed the report against established peer review guidelines. The GPT-4 report shows a significant commitment to transparent AI research, particularly in creating a systems card for risk assessment and mitigation. However, it reveals limitations such as restricted access to training data, inadequate confidence and uncertainty estimations, and concerns over privacy and intellectual property rights. Key strengths identified include the considerable time and economic investment in transparent AI research and the creation of a comprehensive systems card. On the other hand, the lack of clarity in training processes and data raises concerns about encoded biases and interests in GPT-4. The report also lacks confidence and uncertainty estimations, crucial in high-risk areas like healthcare, and fails to address potential privacy and intellectual property issues. Furthermore, this study emphasizes the need for diverse, global involvement in developing and evaluating large language models (LLMs) to ensure broad societal benefits and mitigate risks. The paper presents recommendations such as improving data transparency, developing accountability frameworks, establishing confidence standards for LLM outputs in high-risk settings, and enhancing industry research review processes. It concludes that while GPT-4's report is a step towards open discussions on LLMs, more extensive interdisciplinary reviews are essential for addressing bias, harm, and risk concerns, especially in high-risk domains. The review aims to expand the understanding of LLMs in general and highlights the need for new reflection forms on how LLMs are reviewed, the data required for effective evaluation, and addressing critical issues like bias and risk.
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Affiliation(s)
- Jack Gallifant
- Department of Critical Care, Guy’s & St Thomas’ NHS Trust, London, United Kingdom
- Massachusetts Institute of Technology, Laboratory for Computational Physiology, Cambridge, Massachusetts, United States of America
| | - Amelia Fiske
- Institute of History and Ethics in Medicine, Department of Clinical Medicine, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Yulia A. Levites Strekalova
- Department of Health Services Research, Management, and Policy, College of Public Health and Health Professions, University of Florida, Gainesville, Florida, United States of America
| | - Juan S. Osorio-Valencia
- A.I. and Innovation Committee, Colombian Radiology Association, Medellin, Colombia
- ScienteLab, Bogota, Colombia
- Be4tech, Medellin, Colombia
| | - Rachael Parke
- Cardiothoracic and Vascular Intensive Care Unit, Auckland City Hospital, Auckland, New Zealand
- School of Nursing, The University of Auckland, Auckland, New Zealand
| | - Rogers Mwavu
- Faculty of Computing and Informatics, Mbarara University of Science and Technology, Mbarara, Uganda
| | - Nicole Martinez
- Center for Biomedical Ethics, Stanford University, Stanford, California, United States of America
| | - Judy Wawira Gichoya
- Department of Radiology, Emory University School of Medicine, Atlanta, Georgia, United States of America
| | - Marzyeh Ghassemi
- Massachusetts Institute of Technology, Electrical Engineering and Computer Science (EECS), Cambridge, Massachusetts, United States of America
| | - Dina Demner-Fushman
- National Library of Medicine, NIH, HHS, Bethesda, Maryland, United States of America
| | - Liam G. McCoy
- Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, Canada
| | - Leo Anthony Celi
- Massachusetts Institute of Technology, Laboratory for Computational Physiology, Cambridge, Massachusetts, United States of America
- Division of Pulmonary, Critical Care, and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Robin Pierce
- The Law School, Faculty of Humanities, Arts, and Social Sciences, University of Exeter, Exeter, United Kingdom
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Howard A, Aston S, Gerada A, Reza N, Bincalar J, Mwandumba H, Butterworth T, Hope W, Buchan I. Antimicrobial learning systems: an implementation blueprint for artificial intelligence to tackle antimicrobial resistance. Lancet Digit Health 2024; 6:e79-e86. [PMID: 38123255 DOI: 10.1016/s2589-7500(23)00221-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 10/17/2023] [Accepted: 10/21/2023] [Indexed: 12/23/2023]
Abstract
The proliferation of various forms of artificial intelligence (AI) brings many opportunities to improve health care. AI models can harness complex evolving data, inform and augment human actions, and learn from health outcomes such as morbidity and mortality. The global public health challenge of antimicrobial resistance (AMR) needs large-scale optimisation of antimicrobial use and wider infection care, which could be enabled by carefully constructed AI models. As AI models become increasingly useful and robust, health-care systems remain challenging places for their deployment. An implementation gap exists between the promise of AI models and their use in patient and population care. Here, we outline an adaptive implementation and maintenance framework for AI models to improve antimicrobial use and infection care as a learning system. The roles of AMR problem identification, law and regulation, organisational support, data processing, and AI development, assessment, maintenance, and scalability in the implementation of AMR-targeted AI models are considered.
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Affiliation(s)
- Alex Howard
- Department of Antimicrobial Pharmacodynamics and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK; Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK.
| | - Stephen Aston
- Department of Antimicrobial Pharmacodynamics and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK; Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
| | - Alessandro Gerada
- Department of Antimicrobial Pharmacodynamics and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK; Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
| | - Nada Reza
- Department of Antimicrobial Pharmacodynamics and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK; Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
| | - Jason Bincalar
- Department of Health Data Science, University of Liverpool, Liverpool, UK; Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
| | - Henry Mwandumba
- Malawi Liverpool Wellcome Programme, Kamuzu University of Health Sciences, Blantyre, Malawi; Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK
| | - Tom Butterworth
- Combined Intelligence for Public Health Action, NHS Cheshire and Merseyside, Warrington, UK
| | - William Hope
- Department of Antimicrobial Pharmacodynamics and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK; Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
| | - Iain Buchan
- Department of Public Health, Policy and Systems, Institute of Population Health, University of Liverpool, Liverpool, UK; Combined Intelligence for Public Health Action, NHS Cheshire and Merseyside, Warrington, UK
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Arina P, Kaczorek MR, Hofmaenner DA, Pisciotta W, Refinetti P, Singer M, Mazomenos EB, Whittle J. Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools. Anesthesiology 2024; 140:85-101. [PMID: 37944114 PMCID: PMC11146190 DOI: 10.1097/aln.0000000000004764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Indexed: 11/12/2023]
Abstract
BACKGROUND The utilization of artificial intelligence and machine learning as diagnostic and predictive tools in perioperative medicine holds great promise. Indeed, many studies have been performed in recent years to explore the potential. The purpose of this systematic review is to assess the current state of machine learning in perioperative medicine, its utility in prediction of complications and prognostication, and limitations related to bias and validation. METHODS A multidisciplinary team of clinicians and engineers conducted a systematic review using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) protocol. Multiple databases were searched, including Scopus, Cumulative Index to Nursing and Allied Health Literature (CINAHL), the Cochrane Library, PubMed, Medline, Embase, and Web of Science. The systematic review focused on study design, type of machine learning model used, validation techniques applied, and reported model performance on prediction of complications and prognostication. This review further classified outcomes and machine learning applications using an ad hoc classification system. The Prediction model Risk Of Bias Assessment Tool (PROBAST) was used to assess risk of bias and applicability of the studies. RESULTS A total of 103 studies were identified. The models reported in the literature were primarily based on single-center validations (75%), with only 13% being externally validated across multiple centers. Most of the mortality models demonstrated a limited ability to discriminate and classify effectively. The PROBAST assessment indicated a high risk of systematic errors in predicted outcomes and artificial intelligence or machine learning applications. CONCLUSIONS The findings indicate that the development of this field is still in its early stages. This systematic review indicates that application of machine learning in perioperative medicine is still at an early stage. While many studies suggest potential utility, several key challenges must be first overcome before their introduction into clinical practice. EDITOR’S PERSPECTIVE
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Affiliation(s)
- Pietro Arina
- Bloomsbury Institute of Intensive Care Medicine and Human Physiology and Performance Laboratory, Centre for Perioperative Medicine, Department of Targeted Intervention, University College London, London, United Kingdom
| | - Maciej R. Kaczorek
- Wellcome/EPSRC Centre of Interventional and Surgical Sciences and Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Daniel A. Hofmaenner
- Bloomsbury Institute of Intensive Care Medicine, University College London, London, United Kingdom; and Institute of Intensive Care Medicine, University Hospital Zurich, Zurich, Switzerland
| | - Walter Pisciotta
- Bloomsbury Institute of Intensive Care Medicine, University College London, London, United Kingdom
| | - Patricia Refinetti
- Human Physiology and Performance Laboratory, Centre for Perioperative Medicine, Department of Targeted Intervention, University College London, London, United Kingdom
| | - Mervyn Singer
- Bloomsbury Institute of Intensive Care Medicine, University College London, London, United Kingdom
| | - Evangelos B. Mazomenos
- Wellcome/EPSRC Centre of Interventional and Surgical Sciences and Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - John Whittle
- Human Physiology and Performance Laboratory, Centre for Perioperative Medicine, Department of Targeted Intervention, University College London, London, United Kingdom
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Didi I, Alliot JM, Dumas PY, Vergez F, Tavitian S, Largeaud L, Bidet A, Rieu JB, Luquet I, Lechevalier N, Delabesse E, Sarry A, De Grande AC, Bérard E, Pigneux A, Récher C, Simoncini D, Bertoli S. Artificial intelligence-based prediction models for acute myeloid leukemia using real-life data: A DATAML registry study. Leuk Res 2024; 136:107437. [PMID: 38215555 DOI: 10.1016/j.leukres.2024.107437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 12/19/2023] [Accepted: 01/05/2024] [Indexed: 01/14/2024]
Abstract
We designed artificial intelligence-based prediction models (AIPM) using 52 diagnostic variables from 3687 patients included in the DATAML registry treated with intensive chemotherapy (IC, N = 3030) or azacitidine (AZA, N = 657) for an acute myeloid leukemia (AML). A neural network called multilayer perceptron (MLP) achieved a prediction accuracy for overall survival (OS) of 68.5% and 62.1% in the IC and AZA cohorts, respectively. The Boruta algorithm could select the most important variables for prediction without decreasing accuracy. Thirteen features were retained with this algorithm in the IC cohort: age, cytogenetic risk, white blood cells count, LDH, platelet count, albumin, MPO expression, mean corpuscular volume, CD117 expression, NPM1 mutation, AML status (de novo or secondary), multilineage dysplasia and ASXL1 mutation; and 7 variables in the AZA cohort: blood blasts, serum ferritin, CD56, LDH, hemoglobin, CD13 and disseminated intravascular coagulation (DIC). We believe that AIPM could help hematologists to deal with the huge amount of data available at diagnosis, enabling them to have an OS estimation and guide their treatment choice. Our registry-based AIPM could offer a large real-life dataset with original and exhaustive features and select a low number of diagnostic features with an equivalent accuracy of prediction, more appropriate to routine practice.
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Affiliation(s)
| | | | - Pierre-Yves Dumas
- Centre Hospitalier Universitaire de Bordeaux, Service d'Hématologie Clinique et de Thérapie Cellulaire, Bordeaux, France; Université de Bordeaux, Bordeaux, France; Institut National de la Santé et de la Recherche Médicale, U1035 Bordeaux, France
| | - François Vergez
- Centre Hospitalo-Universitaire de Toulouse, Institut Universitaire du Cancer Toulouse-Oncopole, Laboratoire d'hématologie, Toulouse, France; Centre de Recherches en Cancérologie de Toulouse, Université Toulouse 3 Paul Sabatier, Toulouse, France
| | - Suzanne Tavitian
- Centre Hospitalo-Universitaire de Toulouse, Institut Universitaire du Cancer de Toulouse-Oncopole, Service d'hématologie, Toulouse, France
| | - Laëtitia Largeaud
- Centre Hospitalo-Universitaire de Toulouse, Institut Universitaire du Cancer Toulouse-Oncopole, Laboratoire d'hématologie, Toulouse, France; Centre de Recherches en Cancérologie de Toulouse, Université Toulouse 3 Paul Sabatier, Toulouse, France
| | - Audrey Bidet
- CHU Bordeaux, Laboratoire d'Hématologie Biologique, F-33000 Bordeaux, France
| | - Jean-Baptiste Rieu
- Centre Hospitalo-Universitaire de Toulouse, Institut Universitaire du Cancer Toulouse-Oncopole, Laboratoire d'hématologie, Toulouse, France
| | - Isabelle Luquet
- Centre Hospitalo-Universitaire de Toulouse, Institut Universitaire du Cancer Toulouse-Oncopole, Laboratoire d'hématologie, Toulouse, France
| | - Nicolas Lechevalier
- CHU Bordeaux, Laboratoire d'Hématologie Biologique, F-33000 Bordeaux, France
| | - Eric Delabesse
- Centre Hospitalo-Universitaire de Toulouse, Institut Universitaire du Cancer Toulouse-Oncopole, Laboratoire d'hématologie, Toulouse, France; Centre de Recherches en Cancérologie de Toulouse, Université Toulouse 3 Paul Sabatier, Toulouse, France
| | - Audrey Sarry
- Centre Hospitalo-Universitaire de Toulouse, Institut Universitaire du Cancer de Toulouse-Oncopole, Service d'hématologie, Toulouse, France
| | - Anne-Charlotte De Grande
- Centre Hospitalier Universitaire de Bordeaux, Service d'Hématologie Clinique et de Thérapie Cellulaire, Bordeaux, France
| | - Emilie Bérard
- Department of Epidemiology, Health Economics and Public Health, UMR 1295 CERPOP, University of Toulouse, INSERM, UPS, Toulouse University Hospital (CHU), Toulouse, France
| | - Arnaud Pigneux
- Centre Hospitalier Universitaire de Bordeaux, Service d'Hématologie Clinique et de Thérapie Cellulaire, Bordeaux, France; Université de Bordeaux, Bordeaux, France
| | - Christian Récher
- Centre de Recherches en Cancérologie de Toulouse, Université Toulouse 3 Paul Sabatier, Toulouse, France; Centre Hospitalo-Universitaire de Toulouse, Institut Universitaire du Cancer de Toulouse-Oncopole, Service d'hématologie, Toulouse, France
| | - David Simoncini
- IRIT UMR 5505-CNRS, Université Toulouse I Capitole, Toulouse, France
| | - Sarah Bertoli
- Centre de Recherches en Cancérologie de Toulouse, Université Toulouse 3 Paul Sabatier, Toulouse, France; Centre Hospitalo-Universitaire de Toulouse, Institut Universitaire du Cancer de Toulouse-Oncopole, Service d'hématologie, Toulouse, France.
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Kolasa K, Admassu B, Hołownia-Voloskova M, Kędzior KJ, Poirrier JE, Perni S. Systematic reviews of machine learning in healthcare: a literature review. Expert Rev Pharmacoecon Outcomes Res 2024; 24:63-115. [PMID: 37955147 DOI: 10.1080/14737167.2023.2279107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 10/31/2023] [Indexed: 11/14/2023]
Abstract
INTRODUCTION The increasing availability of data and computing power has made machine learning (ML) a viable approach to faster, more efficient healthcare delivery. METHODS A systematic literature review (SLR) of published SLRs evaluating ML applications in healthcare settings published between1 January 2010 and 27 March 2023 was conducted. RESULTS In total 220 SLRs covering 10,462 ML algorithms were reviewed. The main application of AI in medicine related to the clinical prediction and disease prognosis in oncology and neurology with the use of imaging data. Accuracy, specificity, and sensitivity were provided in 56%, 28%, and 25% SLRs respectively. Internal and external validation was reported in 53% and less than 1% of the cases respectively. The most common modeling approach was neural networks (2,454 ML algorithms), followed by support vector machine and random forest/decision trees (1,578 and 1,522 ML algorithms, respectively). EXPERT OPINION The review indicated considerable reporting gaps in terms of the ML's performance, both internal and external validation. Greater accessibility to healthcare data for developers can ensure the faster adoption of ML algorithms into clinical practice.
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Affiliation(s)
- Katarzyna Kolasa
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
| | - Bisrat Admassu
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
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Adhikari K, Naik N, Hameed BZ, Raghunath SK, Somani BK. Exploring the Ethical, Legal, and Social Implications of ChatGPT in Urology. Curr Urol Rep 2024; 25:1-8. [PMID: 37735339 DOI: 10.1007/s11934-023-01185-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/05/2023] [Indexed: 09/23/2023]
Abstract
PURPOSE OF THE REVIEW ChatGPT is programmed to generate responses based on pattern recognition. With this vast popularity and exponential growth, the question arises of moral issues, security and legitimacy. In this review article, we aim to analyze the ethical and legal implications of using ChatGPT in Urology and explore potential solutions addressing these concerns. RECENT FINDINGS There are many potential applications of ChatGPT in urology, and the extent to which it might improve healthcare may cause a profound shift in the way we deliver our services to patients and the overall healthcare system. This encompasses diagnosis and treatment planning, clinical workflow, patient education, augmenting consultations, and urological research. The ethical and legal considerations include patient autonomy and informed consent, privacy and confidentiality, bias and fairness, human oversight and accountability, trust and transparency, liability and malpractice, intellectual property rights, and regulatory framework. The application of ChatGPT in urology has shown great potential to improve patient care and assist urologists in various aspects of clinical practice, research, and education. Complying with data security and privacy regulations, and ensuring human oversight and accountability are some potential solutions to these legal and ethical concerns. Overall, the benefits and risks of using ChatGPT in urology must be weighed carefully, and a cautious approach must be taken to ensure that its use aligns with human values and advances patient care ethically and responsibly.
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Affiliation(s)
- Kinju Adhikari
- Department of Urology, HCG Cancer Centre, Bangaluru, India
| | - Nithesh Naik
- Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Bm Zeeshan Hameed
- Department of Urology, Father Muller Medical College, Mangalore, Karnataka, India
| | - S K Raghunath
- Department of Urology, HCG Cancer Centre, Bangaluru, India
| | - Bhaskar K Somani
- Department of Urology, University Hospital Southampton NHS Trust, Southampton, SO16 6YD, UK.
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Lou SS, Liu Y, Cohen ME, Ko CY, Hall BL, Kannampallil T. National Multi-Institutional Validation of a Surgical Transfusion Risk Prediction Model. J Am Coll Surg 2024; 238:99-105. [PMID: 37737660 DOI: 10.1097/xcs.0000000000000874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/23/2023]
Abstract
BACKGROUND Accurate estimation of surgical transfusion risk is important for many aspects of surgical planning, yet few methods for estimating are available for estimating such risk. There is a need for reliable validated methods for transfusion risk stratification to support effective perioperative planning and resource stewardship. STUDY DESIGN This study was conducted using the American College of Surgeons NSQIP datafile from 2019. S-PATH performance was evaluated at each contributing hospital, with and without hospital-specific model tuning. Linear regression was used to assess the relationship between hospital characteristics and area under the receiver operating characteristic (AUROC) curve. RESULTS A total of 1,000,927 surgical cases from 414 hospitals were evaluated. Aggregate AUROC was 0.910 (95% CI 0.904 to 0.916) without model tuning and 0.925 (95% CI 0.919 to 0.931) with model tuning. AUROC varied across individual hospitals (median 0.900, interquartile range 0.849 to 0.944), but no statistically significant relationships were found between hospital-level characteristics studied and model AUROC. CONCLUSIONS S-PATH demonstrated excellent discriminative performance, although there was variation across hospitals that was not well-explained by hospital-level characteristics. These results highlight the S-PATH's viability as a generalizable surgical transfusion risk prediction tool.
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Affiliation(s)
- Sunny S Lou
- From the Department of Anesthesiology, Washington University School of Medicine, St Louis, MO (Lou, Kannampallil)
| | - Yaoming Liu
- Division of Research and Optimal Patient Care, American College of Surgeons, Chicago, IL (Liu, Ko, Hall, Cohen)
| | - Mark E Cohen
- Division of Research and Optimal Patient Care, American College of Surgeons, Chicago, IL (Liu, Ko, Hall, Cohen)
| | - Clifford Y Ko
- Division of Research and Optimal Patient Care, American College of Surgeons, Chicago, IL (Liu, Ko, Hall, Cohen)
- Department of Surgery, David Geffen School of Medicine, University of California Los Angeles, and the VA Greater Los Angeles Health System, Los Angeles, CA (Ko)
| | - Bruce L Hall
- Division of Research and Optimal Patient Care, American College of Surgeons, Chicago, IL (Liu, Ko, Hall, Cohen)
- Department of Surgery, Washington University School of Medicine; Center for Health Policy and the Olin Business School at Washington University in St Louis; John Cochran Veterans Affairs Medical Center; and BJC Healthcare, St Louis, MO (Hall)
| | - Thomas Kannampallil
- From the Department of Anesthesiology, Washington University School of Medicine, St Louis, MO (Lou, Kannampallil)
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Xie J, Zhong W, Yang R, Wang L, Zhen X. Discriminative fusion of moments-aligned latent representation of multimodality medical data. Phys Med Biol 2023; 69:015015. [PMID: 38052076 DOI: 10.1088/1361-6560/ad1271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 12/05/2023] [Indexed: 12/07/2023]
Abstract
Fusion of multimodal medical data provides multifaceted, disease-relevant information for diagnosis or prognosis prediction modeling. Traditional fusion strategies such as feature concatenation often fail to learn hidden complementary and discriminative manifestations from high-dimensional multimodal data. To this end, we proposed a methodology for the integration of multimodality medical data by matching their moments in a latent space, where the hidden, shared information of multimodal data is gradually learned by optimization with multiple feature collinearity and correlation constrains. We first obtained the multimodal hidden representations by learning mappings between the original domain and shared latent space. Within this shared space, we utilized several relational regularizations, including data attribute preservation, feature collinearity and feature-task correlation, to encourage learning of the underlying associations inherent in multimodal data. The fused multimodal latent features were finally fed to a logistic regression classifier for diagnostic prediction. Extensive evaluations on three independent clinical datasets have demonstrated the effectiveness of the proposed method in fusing multimodal data for medical prediction modeling.
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Affiliation(s)
- Jincheng Xie
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, People's Republic of China
| | - Weixiong Zhong
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, People's Republic of China
| | - Ruimeng Yang
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510180, People's Republic of China
| | - Linjing Wang
- Radiotherapy Center, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, Guangdong 510095, People's Republic of China
| | - Xin Zhen
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, People's Republic of China
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Staes CJ, Beck AC, Chalkidis G, Scheese CH, Taft T, Guo JW, Newman MG, Kawamoto K, Sloss EA, McPherson JP. Design of an interface to communicate artificial intelligence-based prognosis for patients with advanced solid tumors: a user-centered approach. J Am Med Inform Assoc 2023; 31:174-187. [PMID: 37847666 PMCID: PMC10746322 DOI: 10.1093/jamia/ocad201] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 09/18/2023] [Accepted: 10/02/2023] [Indexed: 10/19/2023] Open
Abstract
OBJECTIVES To design an interface to support communication of machine learning (ML)-based prognosis for patients with advanced solid tumors, incorporating oncologists' needs and feedback throughout design. MATERIALS AND METHODS Using an interdisciplinary user-centered design approach, we performed 5 rounds of iterative design to refine an interface, involving expert review based on usability heuristics, input from a color-blind adult, and 13 individual semi-structured interviews with oncologists. Individual interviews included patient vignettes and a series of interfaces populated with representative patient data and predicted survival for each treatment decision point when a new line of therapy (LoT) was being considered. Ongoing feedback informed design decisions, and directed qualitative content analysis of interview transcripts was used to evaluate usability and identify enhancement requirements. RESULTS Design processes resulted in an interface with 7 sections, each addressing user-focused questions, supporting oncologists to "tell a story" as they discuss prognosis during a clinical encounter. The iteratively enhanced interface both triggered and reflected design decisions relevant when attempting to communicate ML-based prognosis, and exposed misassumptions. Clinicians requested enhancements that emphasized interpretability over explainability. Qualitative findings confirmed that previously identified issues were resolved and clarified necessary enhancements (eg, use months not days) and concerns about usability and trust (eg, address LoT received elsewhere). Appropriate use should be in the context of a conversation with an oncologist. CONCLUSION User-centered design, ongoing clinical input, and a visualization to communicate ML-related outcomes are important elements for designing any decision support tool enabled by artificial intelligence, particularly when communicating prognosis risk.
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Affiliation(s)
- Catherine J Staes
- College of Nursing, University of Utah, Salt Lake City, UT 84112, United States
- Department of Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, UT 84108, United States
| | - Anna C Beck
- Department of Internal Medicine, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, United States
| | - George Chalkidis
- Healthcare IT Research Department, Center for Digital Services, Hitachi Ltd., Tokyo, Japan
| | - Carolyn H Scheese
- College of Nursing, University of Utah, Salt Lake City, UT 84112, United States
- Department of Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, UT 84108, United States
| | - Teresa Taft
- Department of Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, UT 84108, United States
| | - Jia-Wen Guo
- College of Nursing, University of Utah, Salt Lake City, UT 84112, United States
- Department of Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, UT 84108, United States
| | - Michael G Newman
- Department of Population Sciences, Huntsman Cancer Institute, Salt Lake City, UT 84112, United States
| | - Kensaku Kawamoto
- Department of Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, UT 84108, United States
| | - Elizabeth A Sloss
- College of Nursing, University of Utah, Salt Lake City, UT 84112, United States
| | - Jordan P McPherson
- Department of Pharmacotherapy, College of Pharmacy, University of Utah, Salt Lake City, UT 84108, United States
- Department of Pharmacy, Huntsman Cancer Institute, Salt Lake City, UT 84112, United States
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Barreto TDO, Veras NVR, Cardoso PH, Fernandes FRDS, Medeiros LPDS, Bezerra MV, de Andrade FMQ, Pinheiro CDO, Sánchez-Gendriz I, Silva GJPC, Rodrigues LF, de Morais AHF, dos Santos JPQ, Paiva JC, de Andrade IGM, Valentim RADM. Artificial intelligence applied to analyzes during the pandemic: COVID-19 beds occupancy in the state of Rio Grande do Norte, Brazil. Front Artif Intell 2023; 6:1290022. [PMID: 38145230 PMCID: PMC10748397 DOI: 10.3389/frai.2023.1290022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 11/17/2023] [Indexed: 12/26/2023] Open
Abstract
The COVID-19 pandemic is already considered one of the biggest global health crises. In Rio Grande do Norte, a Brazilian state, the RegulaRN platform was the health information system used to regulate beds for patients with COVID-19. This article explored machine learning and deep learning techniques with RegulaRN data in order to identify the best models and parameters to predict the outcome of a hospitalized patient. A total of 25,366 bed regulations for COVID-19 patients were analyzed. The data analyzed comes from the RegulaRN Platform database from April 2020 to August 2022. From these data, the nine most pertinent characteristics were selected from the twenty available, and blank or inconclusive data were excluded. This was followed by the following steps: data pre-processing, database balancing, training, and test. The results showed better performance in terms of accuracy (84.01%), precision (79.57%), and F1-score (81.00%) for the Multilayer Perceptron model with Stochastic Gradient Descent optimizer. The best results for recall (84.67%), specificity (84.67%), and ROC-AUC (91.6%) were achieved by Root Mean Squared Propagation. This study compared different computational methods of machine and deep learning whose objective was to classify bed regulation data for patients with COVID-19 from the RegulaRN Platform. The results have made it possible to identify the best model to help health professionals during the process of regulating beds for patients with COVID-19. The scientific findings of this article demonstrate that the computational methods used applied through a digital health solution, can assist in the decision-making of medical regulators and government institutions in situations of public health crisis.
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Affiliation(s)
- Tiago de Oliveira Barreto
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil
| | - Nícolas Vinícius Rodrigues Veras
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil
- Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte (IFRN), Natal, Rio Grande do Norte, Brazil
| | - Pablo Holanda Cardoso
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil
- Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte (IFRN), Natal, Rio Grande do Norte, Brazil
| | - Felipe Ricardo dos Santos Fernandes
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil
| | | | - Maria Valéria Bezerra
- Secretary of Public Health of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | | | | | - Ignacio Sánchez-Gendriz
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil
| | - Gleyson José Pinheiro Caldeira Silva
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil
- Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte (IFRN), Natal, Rio Grande do Norte, Brazil
| | - Leandro Farias Rodrigues
- Brazilian Company of Hospital Services (EBSERH), University Hospital of Pelotas, Federal University of Pelotas (UFPel), Pelotas, Rio Grande do Sul, Brazil
| | - Antonio Higor Freire de Morais
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil
- Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte (IFRN), Natal, Rio Grande do Norte, Brazil
| | - João Paulo Queiroz dos Santos
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil
- Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte (IFRN), Natal, Rio Grande do Norte, Brazil
| | - Jailton Carlos Paiva
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil
- Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte (IFRN), Natal, Rio Grande do Norte, Brazil
| | - Ion Garcia Mascarenhas de Andrade
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil
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Antão J, de Mast J, Marques A, Franssen FME, Spruit MA, Deng Q. Demystification of artificial intelligence for respiratory clinicians managing patients with obstructive lung diseases. Expert Rev Respir Med 2023; 17:1207-1219. [PMID: 38270524 DOI: 10.1080/17476348.2024.2302940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 01/04/2024] [Indexed: 01/26/2024]
Abstract
INTRODUCTION Asthma and chronic obstructive pulmonary disease (COPD) are leading causes of morbidity and mortality worldwide. Despite all available diagnostics and treatments, these conditions pose a significant individual, economic and social burden. Artificial intelligence (AI) promises to support clinical decision-making processes by optimizing diagnosis and treatment strategies of these heterogeneous and complex chronic respiratory diseases. Its capabilities extend to predicting exacerbation risk, disease progression and mortality, providing healthcare professionals with valuable insights for more effective care. Nevertheless, the knowledge gap between respiratory clinicians and data scientists remains a major constraint for wide application of AI and may hinder future progress. This narrative review aims to bridge this gap and encourage AI deployment by explaining its methodology and added value in asthma and COPD diagnosis and treatment. AREAS COVERED This review offers an overview of the fundamental concepts of AI and machine learning, outlines the key steps in building a model, provides examples of their applicability in asthma and COPD care, and discusses barriers to their implementation. EXPERT OPINION Machine learning can advance our understanding of asthma and COPD, enabling personalized therapy and better outcomes. Further research and validation are needed to ensure the development of clinically meaningful and generalizable models.
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Affiliation(s)
- Joana Antão
- Lab3R - Respiratory Research and Rehabilitation Laboratory, School of Health Sciences, University of Aveiro (ESSUA), Aveiro, Portugal
- iBiMED - Institute of Biomedicine, Department of Medical Sciences, University of Aveiro, Aveiro, Portugal
- Department of Research and Development, Ciro, Horn, The Netherlands
- Department of Respiratory Medicine, Maastricht University Medical Centre, NUTRIM School of Nutrition and Translational Research in Metabolism, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Jeroen de Mast
- Economics and Business, University of Amsterdam, Amsterdam, The Netherlands
| | - Alda Marques
- Lab3R - Respiratory Research and Rehabilitation Laboratory, School of Health Sciences, University of Aveiro (ESSUA), Aveiro, Portugal
- iBiMED - Institute of Biomedicine, Department of Medical Sciences, University of Aveiro, Aveiro, Portugal
| | - Frits M E Franssen
- Department of Research and Development, Ciro, Horn, The Netherlands
- Department of Respiratory Medicine, Maastricht University Medical Centre, NUTRIM School of Nutrition and Translational Research in Metabolism, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Martijn A Spruit
- Department of Research and Development, Ciro, Horn, The Netherlands
- Department of Respiratory Medicine, Maastricht University Medical Centre, NUTRIM School of Nutrition and Translational Research in Metabolism, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Qichen Deng
- Department of Research and Development, Ciro, Horn, The Netherlands
- Department of Respiratory Medicine, Maastricht University Medical Centre, NUTRIM School of Nutrition and Translational Research in Metabolism, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
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