1
|
Trottet C, Allam A, Horvath AN, Finckh A, Hügle T, Adler S, Kyburz D, Micheroli R, Krauthammer M, Ospelt C. Explainable deep learning for disease activity prediction in chronic inflammatory joint diseases. PLOS DIGITAL HEALTH 2024; 3:e0000422. [PMID: 38935600 PMCID: PMC11210792 DOI: 10.1371/journal.pdig.0000422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 05/27/2024] [Indexed: 06/29/2024]
Abstract
Analysing complex diseases such as chronic inflammatory joint diseases (CIJDs), where many factors influence the disease evolution over time, is a challenging task. CIJDs are rheumatic diseases that cause the immune system to attack healthy organs, mainly the joints. Different environmental, genetic and demographic factors affect disease development and progression. The Swiss Clinical Quality Management in Rheumatic Diseases (SCQM) Foundation maintains a national database of CIJDs documenting the disease management over time for 19'267 patients. We propose the Disease Activity Score Network (DAS-Net), an explainable multi-task learning model trained on patients' data with different arthritis subtypes, transforming longitudinal patient journeys into comparable representations and predicting multiple disease activity scores. First, we built a modular model composed of feed-forward neural networks, long short-term memory networks and attention layers to process the heterogeneous patient histories and predict future disease activity. Second, we investigated the utility of the model's computed patient representations (latent embeddings) to identify patients with similar disease progression. Third, we enhanced the explainability of our model by analysing the impact of different patient characteristics on disease progression and contrasted our model outcomes with medical expert knowledge. To this end, we explored multiple feature attribution methods including SHAP, attention attribution and feature weighting using case-based similarity. Our model outperforms temporal and non-temporal neural network, tree-based, and naive static baselines in predicting future disease activity scores. To identify similar patients, a k-nearest neighbours regression algorithm applied to the model's computed latent representations outperforms baseline strategies that use raw input features representation.
Collapse
Affiliation(s)
- Cécile Trottet
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Ahmed Allam
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Aron N. Horvath
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Axel Finckh
- Division of Rheumatology, Department of Medicine, Faculty of Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Thomas Hügle
- Department of Rheumatology, Lausanne University Hospital, Lausanne, Switzerland
| | - Sabine Adler
- Department of Rheumatology and Immunology, Kantonsspital Aarau, Aarau, Switzerland
- Department of Rheumatology and Immunology, Inselspital - University Hospital Bern, Bern, Switzerland
| | - Diego Kyburz
- Department of Rheumatology, University Hospital Basel, Basel, Switzerland
| | - Raphael Micheroli
- Center of Experimental Rheumatology, Department of Rheumatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Michael Krauthammer
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Biomedical Informatics DFL, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Caroline Ospelt
- Center of Experimental Rheumatology, Department of Rheumatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| |
Collapse
|
2
|
Sivarajkumar S, Mohammad HA, Oniani D, Roberts K, Hersh W, Liu H, He D, Visweswaran S, Wang Y. Clinical Information Retrieval: A Literature Review. JOURNAL OF HEALTHCARE INFORMATICS RESEARCH 2024; 8:313-352. [PMID: 38681755 PMCID: PMC11052968 DOI: 10.1007/s41666-024-00159-4] [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: 03/28/2023] [Revised: 12/07/2023] [Accepted: 01/08/2024] [Indexed: 05/01/2024]
Abstract
Clinical information retrieval (IR) plays a vital role in modern healthcare by facilitating efficient access and analysis of medical literature for clinicians and researchers. This scoping review aims to offer a comprehensive overview of the current state of clinical IR research and identify gaps and potential opportunities for future studies in this field. The main objective was to assess and analyze the existing literature on clinical IR, focusing on the methods, techniques, and tools employed for effective retrieval and analysis of medical information. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted an extensive search across databases such as Ovid Embase, Ovid Medline, Scopus, ACM Digital Library, IEEE Xplore, and Web of Science, covering publications from January 1, 2010, to January 4, 2023. The rigorous screening process led to the inclusion of 184 papers in our review. Our findings provide a detailed analysis of the clinical IR research landscape, covering aspects like publication trends, data sources, methodologies, evaluation metrics, and applications. The review identifies key research gaps in clinical IR methods such as indexing, ranking, and query expansion, offering insights and opportunities for future studies in clinical IR, thus serving as a guiding framework for upcoming research efforts in this rapidly evolving field. The study also underscores an imperative for innovative research on advanced clinical IR systems capable of fast semantic vector search and adoption of neural IR techniques for effective retrieval of information from unstructured electronic health records (EHRs). Supplementary Information The online version contains supplementary material available at 10.1007/s41666-024-00159-4.
Collapse
Affiliation(s)
| | | | - David Oniani
- Department of Health Information Management, University of Pittsburgh, Pittsburgh, PA USA
| | - Kirk Roberts
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX USA
| | - William Hersh
- Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, OR USA
| | - Hongfang Liu
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX USA
| | - Daqing He
- Department of Information Science, University of Pittsburgh, Pittsburgh, PA USA
| | - Shyam Visweswaran
- Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA USA
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA USA
- Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, PA USA
| | - Yanshan Wang
- Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA USA
- Department of Health Information Management, University of Pittsburgh, Pittsburgh, PA USA
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA USA
- Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, PA USA
| |
Collapse
|
3
|
Michel J, Manns A, Boudersa S, Jaubert C, Dupic L, Vivien B, Burgun A, Campeotto F, Tsopra R. Clinical decision support system in emergency telephone triage: A scoping review of technical design, implementation and evaluation. Int J Med Inform 2024; 184:105347. [PMID: 38290244 DOI: 10.1016/j.ijmedinf.2024.105347] [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: 12/18/2023] [Revised: 01/09/2024] [Accepted: 01/21/2024] [Indexed: 02/01/2024]
Abstract
OBJECTIVES Emergency department overcrowding could be improved by upstream telephone triage. Emergency telephone triage aims at managing and orientating adequately patients as early as possible and distributing limited supply of staff and materials. This complex task could be improved with the use of Clinical decision support systems (CDSS). The aim of this scoping review was to identify literature gaps for the future development and evaluation of CDSS for Emergency telephone triage. MATERIALS AND METHODS We present here a scoping review of CDSS designed for emergency telephone triage, and compared them in terms of functional characteristics, technical design, health care implementation and methodologies used for evaluation, following the PRISMA-ScR guidelines. RESULTS Regarding design, 19 CDSS were retrieved: 12 were knowledge based CDSS (decisional algorithms built according to guidelines or clinical expertise) and 7 were data driven (statistical, machine learning, or deep learning models). Most of them aimed at assisting nurses or non-medical staff by providing patient orientation and/or severity/priority assessment. Eleven were implemented in real life, and only three were connected to the Electronic Health Record. Regarding evaluation, CDSS were assessed through various aspects: intrinsic characteristics, impact on clinical practice or user apprehension. Only one pragmatic trial and one randomized controlled trial were conducted. CONCLUSION This review highlights the potential of a hybrid system, user tailored, flexible, connected to the electronic health record, which could work with oral, video and digital data; and the need to evaluate CDSS on intrinsic characteristics and impact on clinical practice, iteratively at each distinct stage of the IT lifecycle.
Collapse
Affiliation(s)
- Julie Michel
- SAMU 93-UF Recherche-Enseignement-Qualité, Université Paris 13, Sorbonne Paris Cité, Inserm U942, Hôpital Avicenne, 125, rue de Stalingrad, 93009 Bobigny, France
| | - Aurélia Manns
- Université Paris Cité, Sorbonne Université, Inserm, Centre de Recherche des Cordeliers, F-75006 Paris, France; Department of Medical Informatics, AP-HP, Hôpital Européen Georges-Pompidou et Hôpital Necker-Enfants Malades, F-75015 Paris, France.
| | - Sofia Boudersa
- Department of Medical Informatics, AP-HP, Hôpital Européen Georges-Pompidou et Hôpital Necker-Enfants Malades, F-75015 Paris, France
| | - Côme Jaubert
- Université Paris Cité, Sorbonne Université, Inserm, Centre de Recherche des Cordeliers, F-75006 Paris, France
| | - Laurent Dupic
- Régulation Régionale Pédiatrique, SAMU de Paris, Hôpital Necker - Enfants Malades, AP-HP, Paris, France
| | - Benoit Vivien
- Digital Health Program of Université de Paris Cité, Paris, France; Régulation Régionale Pédiatrique, SAMU de Paris, Hôpital Necker - Enfants Malades, AP-HP, Paris, France
| | - Anita Burgun
- Université Paris Cité, Sorbonne Université, Inserm, Centre de Recherche des Cordeliers, F-75006 Paris, France; Department of Medical Informatics, AP-HP, Hôpital Européen Georges-Pompidou et Hôpital Necker-Enfants Malades, F-75015 Paris, France
| | - Florence Campeotto
- Digital Health Program of Université de Paris Cité, Paris, France; Régulation Régionale Pédiatrique, SAMU de Paris, Hôpital Necker - Enfants Malades, AP-HP, Paris, France; Faculté de Pharmacie, Université de Paris Cité, Inserm UMR S1139, Paris, France
| | - Rosy Tsopra
- Université Paris Cité, Sorbonne Université, Inserm, Centre de Recherche des Cordeliers, F-75006 Paris, France; Department of Medical Informatics, AP-HP, Hôpital Européen Georges-Pompidou et Hôpital Necker-Enfants Malades, F-75015 Paris, France
| |
Collapse
|
4
|
Ameri A, Ameri A, Salmanizadeh F, Bahaadinbeigy K. Clinical decision support systems (CDSS) in assistance to COVID-19 diagnosis: A scoping review on types and evaluation methods. Health Sci Rep 2024; 7:e1919. [PMID: 38384976 PMCID: PMC10879639 DOI: 10.1002/hsr2.1919] [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: 04/25/2023] [Revised: 01/30/2024] [Accepted: 02/01/2024] [Indexed: 02/23/2024] Open
Abstract
Background and Aims Due to the COVID-19 pandemic, a precise and reliable diagnosis of this disease is critical. The use of clinical decision support systems (CDSS) can help facilitate the diagnosis of COVID-19. This scoping review aimed to investigate the role of CDSS in diagnosing COVID-19. Methods We searched four databases (Web of Science, PubMed, Scopus, and Embase) using three groups of keywords related to CDSS, COVID-19, and diagnosis. To collect data from studies, we utilized a data extraction form that consisted of eight fields. Three researchers selected relevant articles and extracted data using a data collection form. To resolve any disagreements, we consulted with a fourth researcher. Results A search of the databases retrieved 2199 articles, of which 68 were included in this review after removing duplicates and irrelevant articles. The studies used nonknowledge-based CDSS (n = 52) and knowledge-based CDSS (n = 16). Convolutional Neural Networks (CNN) (n = 33) and Support Vector Machine (SVM) (n = 8) were employed to design the CDSS in most of the studies. Accuracy (n = 43) and sensitivity (n = 35) were the most common metrics for evaluating CDSS. Conclusion CDSS for COVID-19 diagnosis have been developed mainly through machine learning (ML) methods. The greater use of these techniques can be due to their availability of public data sets about chest imaging. Although these studies indicate high accuracy for CDSS based on ML, their novelty and data set biases raise questions about replacing these systems as clinician assistants in decision-making. Further studies are needed to improve and compare the robustness and reliability of nonknowledge-based and knowledge-based CDSS in COVID-19 diagnosis.
Collapse
Affiliation(s)
- Arefeh Ameri
- Health Information Sciences Department, Faculty of Management and Medical Information SciencesKerman University of Medical SciencesKermanIran
| | - Atefeh Ameri
- Pharmaceutical Sciences and Cosmetic Products Research CenterKerman University of Medical SciencesKermanIran
| | - Farzad Salmanizadeh
- Medical Informatics Research Center, Institute for Futures Studies in HealthKerman University of Medical SciencesKermanIran
| | - Kambiz Bahaadinbeigy
- Digital Health TeamAustralian College of Rural and Remote MedicineBrisbaneQueenslandAustralia
| |
Collapse
|
5
|
Hu J, Huang Z, Ge X, Shen Y, Xu Y, Zhang Z, Zhou G, Wang J, Lu S, Yu Y, Wan C, Zhang X, Huang R, Liu Y, Cheng G. Development and application of Chinese medical ontology for diabetes mellitus. BMC Med Inform Decis Mak 2024; 24:18. [PMID: 38243204 PMCID: PMC10799385 DOI: 10.1186/s12911-023-02405-y] [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: 10/05/2023] [Accepted: 12/12/2023] [Indexed: 01/21/2024] Open
Abstract
OBJECTIVE To develop a Chinese Diabetes Mellitus Ontology (CDMO) and explore methods for constructing high-quality Chinese biomedical ontologies. MATERIALS AND METHODS We used various data sources, including Chinese clinical practice guidelines, expert consensus, literature, and hospital information system database schema, to build the CDMO. We combined top-down and bottom-up strategies and integrated text mining and cross-lingual ontology mapping. The ontology was validated by clinical experts and ontology development tools, and its application was validated through clinical decision support and Chinese natural language medical question answering. RESULTS The current CDMO consists of 3,752 classes, 182 fine-grained object properties with hierarchical relationships, 108 annotation properties, and over 12,000 mappings to other well-known medical ontologies in English. Based on the CDMO and clinical practice guidelines, we developed 200 rules for diabetes diagnosis, treatment, diet, and medication recommendations using the Semantic Web Rule Language. By injecting ontology knowledge, CDMO enhances the performance of the T5 model on a real-world Chinese medical question answering dataset related to diabetes. CONCLUSION CDMO has fine-grained semantic relationships and extensive annotation information, providing a foundation for medical artificial intelligence applications in Chinese contexts, including the construction of medical knowledge graphs, clinical decision support systems, and automated medical question answering. Furthermore, the development process incorporated natural language processing and cross-lingual ontology mapping to improve the quality of the ontology and improved development efficiency. This workflow offers a methodological reference for the efficient development of other high-quality Chinese as well as non-English medical ontologies.
Collapse
Affiliation(s)
- Jie Hu
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Zixian Huang
- State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
| | - Xuewen Ge
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Yulin Shen
- State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
| | - Yihan Xu
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Zirui Zhang
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Guangyin Zhou
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Junjie Wang
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Shan Lu
- Outpatient Department of the First Affiliated Hospital of Nanjing Medical University, No.300 Guang Zhou Road, Nanjing, Jiangsu, China
| | - Yun Yu
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Cheng Wan
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Xin Zhang
- Department of Information, the First Affiliated Hospital, Nanjing Medical University, No.300 Guang Zhou Road, Nanjing, Jiangsu, China
| | - Ruochen Huang
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Yun Liu
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China.
- Department of Information, the First Affiliated Hospital, Nanjing Medical University, No.300 Guang Zhou Road, Nanjing, Jiangsu, China.
- Institute of Medical Informatics and Management, Nanjing Medical University, No.300 Guang Zhou Road, Nanjing, Jiangsu, China.
| | - Gong Cheng
- State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China.
| |
Collapse
|
6
|
Wang Z, Liu J, Tian Y, Zhou T, Liu Q, Qiu Y, Li J. Integrating Medical Domain Knowledge for Early Diagnosis of Fever of Unknown Origin: An Interpretable Hierarchical Multimodal Neural Network Approach. IEEE J Biomed Health Inform 2023; 27:5237-5248. [PMID: 37590111 DOI: 10.1109/jbhi.2023.3306041] [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: 08/19/2023]
Abstract
Accurate and interpretable differential diagnostic technologies are crucial for supporting clinicians in decision-making and treatment-planning for patients with fever of unknown origin (FUO). Existing solutions commonly address the diagnosis of FUO by transforming it into a multi-classification task. However, after the emergence of COVID-19 pandemic, clinicians have recognized the heightened significance of early diagnosis in patients with FUO, particularly for practical needs such as early triage. This has resulted in increased demands for identifying a wider range of etiologies, shorter observation windows, and better model interpretability. In this article, we propose an interpretable hierarchical multimodal neural network framework (iHMNNF) to facilitate early diagnosis of FUO by incorporating medical domain knowledge and leveraging multimodal clinical data. The iHMNNF comprises a top-down hierarchical reasoning framework (Td-HRF) built on the class hierarchy of FUO etiologies, five local attention-based multimodal neural networks (La-MNNs) trained for each parent node of the class hierarchy, and an interpretable module based on layer-wise relevance propagation (LRP) and attention mechanism. Experimental datasets were collected from electronic health records (EHRs) at a large-scale tertiary grade-A hospital in China, comprising 34,051 hospital admissions of 30,794 FUO patients from January 2011 to October 2020. Our proposed La-MNNs achieved area under the receiver operating characteristic curve (AUROC) values ranging from 0.7809 to 0.9035 across all five decomposed tasks, surpassing competing machine learning (ML) and single-modality deep learning (DL) methods while also providing enhanced interpretability. Furthermore, we explored the feasibility of identifying FUO etiologies using only the first N-hour time series data obtained after admission.
Collapse
|
7
|
Stirnemann JJ, Besson R, Spaggiari E, Rojo S, Loge F, Peyro-Saint-Paul H, Allassonniere S, Le Pennec E, Hutchinson C, Sebire N, Ville Y. Development and clinical validation of real-time artificial intelligence diagnostic companion for fetal ultrasound examination. ULTRASOUND IN OBSTETRICS & GYNECOLOGY : THE OFFICIAL JOURNAL OF THE INTERNATIONAL SOCIETY OF ULTRASOUND IN OBSTETRICS AND GYNECOLOGY 2023; 62:353-360. [PMID: 37161503 DOI: 10.1002/uog.26242] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 02/13/2023] [Accepted: 03/20/2023] [Indexed: 05/11/2023]
Abstract
OBJECTIVE Prenatal diagnosis of a rare disease on ultrasound relies on a physician's ability to remember an intractable amount of knowledge. We developed a real-time decision support system (DSS) that suggests, at each step of the examination, the next phenotypic feature to assess, optimizing the diagnostic pathway to the smallest number of possible diagnoses. The objective of this study was to evaluate the performance of this real-time DSS using clinical data. METHODS This validation study was conducted on a database of 549 perinatal phenotypes collected from two referral centers (one in France and one in the UK). Inclusion criteria were: at least one anomaly was visible on fetal ultrasound after 11 weeks' gestation; the anomaly was confirmed postnatally; an associated rare disease was confirmed or ruled out based on postnatal/postmortem investigation, including physical examination, genetic testing and imaging; and, when confirmed, the syndrome was known by the DSS software. The cases were assessed retrospectively by the software, using either the full phenotype as a single input, or a stepwise input of phenotypic features, as prompted by the software, mimicking its use in a real-life clinical setting. Adjudication of discordant cases, in which there was disagreement between the DSS output and the postnatally confirmed ('ascertained') diagnosis, was performed by a panel of external experts. The proportion of ascertained diagnoses within the software's top-10 differential diagnoses output was evaluated, as well as the sensitivity and specificity of the software to select correctly as its best guess a syndromic or isolated condition. RESULTS The dataset covered 110/408 (27%) diagnoses within the software's database, yielding a cumulative prevalence of 83%. For syndromic cases, the ascertained diagnosis was within the top-10 list in 93% and 83% of cases using the full-phenotype and stepwise input, respectively, after adjudication. The full-phenotype and stepwise approaches were associated, respectively, with a specificity of 94% and 96% and a sensitivity of 99% and 84%. The stepwise approach required an average of 13 queries to reach the final set of diagnoses. CONCLUSIONS The DSS showed high performance when applied to real-world data. This validation study suggests that such software can improve perinatal care, efficiently providing complex and otherwise overlooked knowledge to care-providers involved in ultrasound-based prenatal diagnosis. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.
Collapse
Affiliation(s)
- J J Stirnemann
- Department of Obstetrics and Maternal-Fetal Medicine, Necker-Enfants Malades Hospital, AP-HP, Paris, France
- EA7328 Université de Paris, IMAGINE Institute, Paris, France
| | | | - E Spaggiari
- Department of Obstetrics and Maternal-Fetal Medicine, Necker-Enfants Malades Hospital, AP-HP, Paris, France
- EA7328 Université de Paris, IMAGINE Institute, Paris, France
- Department of Histology-Embryology and Cytogenetics, Unit of Embryo and Fetal Pathology, Necker-Enfants Malades Hospital, AP-HP, Paris, France
| | | | | | | | - S Allassonniere
- School of Medicine, Université de Paris, INRIA EPI HEKA, INSERM UMR 1138, Sorbonne Université, Paris, France
- Center for Applied Mathematics, Ecole Polytechnique, Institut Polytechnique de Paris, Paris, France
| | - E Le Pennec
- Center for Applied Mathematics, Ecole Polytechnique, Institut Polytechnique de Paris, Paris, France
- Xpop, INRIA Saclay Center, Paris, France
| | - C Hutchinson
- NIHR Great Ormond Street Hospital Biomedical Research Centre, London, UK
| | - N Sebire
- NIHR Great Ormond Street Hospital Biomedical Research Centre, London, UK
| | - Y Ville
- Department of Obstetrics and Maternal-Fetal Medicine, Necker-Enfants Malades Hospital, AP-HP, Paris, France
- EA7328 Université de Paris, IMAGINE Institute, Paris, France
| |
Collapse
|
8
|
Chiou SY, Liu LS, Lee CW, Kim DH, Al-masni MA, Liu HL, Wei KC, Yan JL, Chen PY. Augmented Reality Surgical Navigation System Integrated with Deep Learning. Bioengineering (Basel) 2023; 10:617. [PMID: 37237687 PMCID: PMC10215407 DOI: 10.3390/bioengineering10050617] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 05/11/2023] [Accepted: 05/17/2023] [Indexed: 05/28/2023] Open
Abstract
Most current surgical navigation methods rely on optical navigators with images displayed on an external screen. However, minimizing distractions during surgery is critical and the spatial information displayed in this arrangement is non-intuitive. Previous studies have proposed combining optical navigation systems with augmented reality (AR) to provide surgeons with intuitive imaging during surgery, through the use of planar and three-dimensional imagery. However, these studies have mainly focused on visual aids and have paid relatively little attention to real surgical guidance aids. Moreover, the use of augmented reality reduces system stability and accuracy, and optical navigation systems are costly. Therefore, this paper proposed an augmented reality surgical navigation system based on image positioning that achieves the desired system advantages with low cost, high stability, and high accuracy. This system also provides intuitive guidance for the surgical target point, entry point, and trajectory. Once the surgeon uses the navigation stick to indicate the position of the surgical entry point, the connection between the surgical target and the surgical entry point is immediately displayed on the AR device (tablet or HoloLens glasses), and a dynamic auxiliary line is shown to assist with incision angle and depth. Clinical trials were conducted for EVD (extra-ventricular drainage) surgery, and surgeons confirmed the system's overall benefit. A "virtual object automatic scanning" method is proposed to achieve a high accuracy of 1 ± 0.1 mm for the AR-based system. Furthermore, a deep learning-based U-Net segmentation network is incorporated to enable automatic identification of the hydrocephalus location by the system. The system achieves improved recognition accuracy, sensitivity, and specificity of 99.93%, 93.85%, and 95.73%, respectively, representing a significant improvement from previous studies.
Collapse
Affiliation(s)
- Shin-Yan Chiou
- Department of Electrical Engineering, College of Engineering, Chang Gung University, Kwei-Shan, Taoyuan 333, Taiwan
- Department of Nuclear Medicine, Linkou Chang Gung Memorial Hospital, Taoyuan 333, Taiwan
- Department of Neurosurgery, Keelung Chang Gung Memorial Hospital, Keelung 204, Taiwan
| | - Li-Sheng Liu
- Department of Electrical Engineering, College of Engineering, Chang Gung University, Kwei-Shan, Taoyuan 333, Taiwan
- Department of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seodaemun-gu, Seoul 03722, Republic of Korea
| | - Chia-Wei Lee
- Department of Electrical Engineering, College of Engineering, Chang Gung University, Kwei-Shan, Taoyuan 333, Taiwan
| | - Dong-Hyun Kim
- Department of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seodaemun-gu, Seoul 03722, Republic of Korea
| | - Mohammed A. Al-masni
- Department of Artificial Intelligence, College of Software & Convergence Technology, Daeyang AI Center, Sejong University, Seoul 05006, Republic of Korea
| | - Hao-Li Liu
- Department of Electrical Engineering, National Taiwan University, Taipei 106, Taiwan
| | - Kuo-Chen Wei
- New Taipei City Tucheng Hospital, Tao-Yuan, Tucheng, New Taipei City 236, Taiwan
| | - Jiun-Lin Yan
- Department of Neurosurgery, Keelung Chang Gung Memorial Hospital, Keelung 204, Taiwan
| | - Pin-Yuan Chen
- Department of Neurosurgery, Keelung Chang Gung Memorial Hospital, Keelung 204, Taiwan
| |
Collapse
|
9
|
Nabukenya J, Drumright L, Alunyu AE, Semwanga AR. Critical risk and success factors for sustainability of an electronic health data capture, processing and dissemination platform for Uganda. Health Informatics J 2023; 29:14604582231180576. [PMID: 37256870 DOI: 10.1177/14604582231180576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Several studies have investigated challenges that have marred success or even caused the failure of eHealth implementations in Uganda; however, none has focused on the risks and success factors of their sustainability. This study explored critical risk and success factors for the sustainability of an electronic health data capture, processing and dissemination platform for Uganda. A mixed-method research design was followed involving collecting empirical data from all four regions of Uganda. A purposive sampling strategy was used to select the study districts per region, health facilities per district, and respondents/participants per facility or district. Findings revealed several risks and success factors for sustainability, including; bad leadership, corruption, lack of sustainable maintenance programs, lack of suitable sustainability plans, lack of ICT infrastructure investment, poor management systems, funds, stakeholder buy-ins, data sharing and access rights. The success factors included reinvestments as a partial sustainability plan for ICT infrastructure. These factors can be leveraged to ensure the continued operation of eHealth implementations in Uganda. Every electronic health project aiming at success should always make due consideration/sustainability plan at the onset of project conceptualisation; as lack of such a plan has often resulted in failed projects after the initial funds have been withdrawn.
Collapse
Affiliation(s)
- Josephine Nabukenya
- Department of Information Systems, School of Computing and Informatics Technology, Makerere University, Kampala, Uganda
| | - Lydia Drumright
- Department of Medicine, University of Cambridge, Cambridge, UK
| | - Andrew Egwar Alunyu
- Department of Information Systems, School of Computing and Informatics Technology, Makerere University, Kampala, Uganda
| | - Agnes Rwashana Semwanga
- Department of Information Systems, School of Computing and Informatics Technology, Makerere University, Kampala, Uganda
| |
Collapse
|
10
|
Chiu PC, Su KW, Wang CH, Ruan CW, Shiao ZP, Tsao CH, Huang HH. Development and Testing of the Smart Healthcare Prototype System through COVID-19 Patient Innovation. Healthcare (Basel) 2023; 11:healthcare11060847. [PMID: 36981502 PMCID: PMC10048738 DOI: 10.3390/healthcare11060847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 03/02/2023] [Accepted: 03/06/2023] [Indexed: 03/16/2023] Open
Abstract
Since the outbreak of the novel coronavirus disease 2019 (COVID-19), the epidemic has gradually slowed down in various countries and people’s lives have gradually returned to normal. To monitor the spread of the epidemic, studies discussing the design of related healthcare information systems have been increasing recently. However, these studies might not consider the aspect of user-centric design when developing healthcare information systems. This study examined these innovative technology applications and rapidly built prototype systems for smart healthcare through a systematic literature review and a study of patient innovation. The design guidelines for the Smart Healthcare System (SHS) were then compiled through an expert review process. This will provide a reference for future research and similar healthcare information system development.
Collapse
Affiliation(s)
- Po-Chih Chiu
- College of Management, National Kaohsiung University of Science and Technology, Kaohsiung 824005, Taiwan
| | - Kuo-Wei Su
- Department of Information Management, National Kaohsiung University of Science and Technology, Kaohsiung 824005, Taiwan
- Correspondence: (K.-W.S.); (C.-H.T.)
| | - Chao-Hung Wang
- Department of Information Management, National Kaohsiung University of Science and Technology, Kaohsiung 824005, Taiwan
| | - Cong-Wen Ruan
- Department of Information Management, National Kaohsiung University of Science and Technology, Kaohsiung 824005, Taiwan
| | - Zong-Peng Shiao
- Department of Information Management, National Kaohsiung University of Science and Technology, Kaohsiung 824005, Taiwan
| | - Chien-Han Tsao
- Department of Otolaryngology, Chung Shan Medical University Hospital and School of Medicine, Taichung 40201, Taiwan
- Correspondence: (K.-W.S.); (C.-H.T.)
| | - Hsin-Hsin Huang
- Department of Otolaryngology, Chung Shan Medical University Hospital and School of Medicine, Taichung 40201, Taiwan
| |
Collapse
|
11
|
Winter PD, Carusi A. (De)troubling transparency: artificial intelligence (AI) for clinical applications. MEDICAL HUMANITIES 2023; 49:17-26. [PMID: 35545432 PMCID: PMC9985768 DOI: 10.1136/medhum-2021-012318] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/05/2022] [Indexed: 06/15/2023]
Abstract
Artificial intelligence (AI) and machine learning (ML) techniques occupy a prominent role in medical research in terms of the innovation and development of new technologies. However, while many perceive AI as a technology of promise and hope-one that is allowing for more early and accurate diagnosis-the acceptance of AI and ML technologies in hospitals remains low. A major reason for this is the lack of transparency associated with these technologies, in particular epistemic transparency, which results in AI disturbing or troubling established knowledge practices in clinical contexts. In this article, we describe the development process of one AI application for a clinical setting. We show how epistemic transparency is negotiated and co-produced in close collaboration between AI developers and clinicians and biomedical scientists, forming the context in which AI is accepted as an epistemic operator. Drawing on qualitative research with collaborative researchers developing an AI technology for the early diagnosis of a rare respiratory disease (pulmonary hypertension/PH), this paper examines how including clinicians and clinical scientists in the collaborative practices of AI developers de-troubles transparency. Our research shows how de-troubling transparency occurs in three dimensions of AI development relating to PH: querying of data sets, building software and training the model The close collaboration results in an AI application that is at once social and technological: it integrates and inscribes into the technology the knowledge processes of the different participants in its development. We suggest that it is a misnomer to call these applications 'artificial' intelligence, and that they would be better developed and implemented if they were reframed as forms of sociotechnical intelligence.
Collapse
Affiliation(s)
- Peter David Winter
- School of Sociology, Politics and International Studies, University of Bristol, Bristol, UK
| | - Annamaria Carusi
- Interchange Research, London, UK
- Department of Science and Technology Studies, University College London, London, London, UK
| |
Collapse
|
12
|
Vodanović M, Subašić M, Milošević D, Savić Pavičin I. Artificial Intelligence in Medicine and Dentistry. Acta Stomatol Croat 2023; 57:70-84. [PMID: 37288152 PMCID: PMC10243707 DOI: 10.15644/asc57/1/8] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 03/01/2023] [Indexed: 09/14/2023] Open
Abstract
INTRODUCTION Artificial intelligence has been applied in various fields throughout history, but its integration into daily life is more recent. The first applications of AI were primarily in academia and government research institutions, but as technology has advanced, AI has also been applied in industry, commerce, medicine and dentistry. OBJECTIVE Considering that the possibilities of applying artificial intelligence are developing rapidly and that this field is one of the areas with the greatest increase in the number of newly published articles, the aim of this paper was to provide an overview of the literature and to give an insight into the possibilities of applying artificial intelligence in medicine and dentistry. In addition, the aim was to discuss its advantages and disadvantages. CONCLUSION The possibilities of applying artificial intelligence to medicine and dentistry are just being discovered. Artificial intelligence will greatly contribute to developments in medicine and dentistry, as it is a tool that enables development and progress, especially in terms of personalized healthcare that will lead to much better treatment outcomes.
Collapse
Affiliation(s)
- Marin Vodanović
- Department of Dental Anthropology, School of Dental Medicine, University of Zagreb, Croatia
- University Hospital Centre Zagreb, Croatia
| | - Marko Subašić
- Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia
| | - Denis Milošević
- Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia
| | - Ivana Savić Pavičin
- Department of Dental Anthropology, School of Dental Medicine, University of Zagreb, Croatia
- University Hospital Centre Zagreb, Croatia
| |
Collapse
|
13
|
Xu Q, Xie W, Liao B, Hu C, Qin L, Yang Z, Xiong H, Lyu Y, Zhou Y, Luo A. Interpretability of Clinical Decision Support Systems Based on Artificial Intelligence from Technological and Medical Perspective: A Systematic Review. JOURNAL OF HEALTHCARE ENGINEERING 2023; 2023:9919269. [PMID: 36776958 PMCID: PMC9918364 DOI: 10.1155/2023/9919269] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 05/05/2022] [Accepted: 11/24/2022] [Indexed: 02/05/2023]
Abstract
Background Artificial intelligence (AI) has developed rapidly, and its application extends to clinical decision support system (CDSS) for improving healthcare quality. However, the interpretability of AI-driven CDSS poses significant challenges to widespread application. Objective This study is a review of the knowledge-based and data-based CDSS literature regarding interpretability in health care. It highlights the relevance of interpretability for CDSS and the area for improvement from technological and medical perspectives. Methods A systematic search was conducted on the interpretability-related literature published from 2011 to 2020 and indexed in the five databases: Web of Science, PubMed, ScienceDirect, Cochrane, and Scopus. Journal articles that focus on the interpretability of CDSS were included for analysis. Experienced researchers also participated in manually reviewing the selected articles for inclusion/exclusion and categorization. Results Based on the inclusion and exclusion criteria, 20 articles from 16 journals were finally selected for this review. Interpretability, which means a transparent structure of the model, a clear relationship between input and output, and explainability of artificial intelligence algorithms, is essential for CDSS application in the healthcare setting. Methods for improving the interpretability of CDSS include ante-hoc methods such as fuzzy logic, decision rules, logistic regression, decision trees for knowledge-based AI, and white box models, post hoc methods such as feature importance, sensitivity analysis, visualization, and activation maximization for black box models. A number of factors, such as data type, biomarkers, human-AI interaction, needs of clinicians, and patients, can affect the interpretability of CDSS. Conclusions The review explores the meaning of the interpretability of CDSS and summarizes the current methods for improving interpretability from technological and medical perspectives. The results contribute to the understanding of the interpretability of CDSS based on AI in health care. Future studies should focus on establishing formalism for defining interpretability, identifying the properties of interpretability, and developing an appropriate and objective metric for interpretability; in addition, the user's demand for interpretability and how to express and provide explanations are also the directions for future research.
Collapse
Affiliation(s)
- Qian Xu
- The Second Xiangya Hospital of Central South University, No. 139, Renmin Road Central, Changsha, Hunan, China
- School of Life Sciences, Central South University, Changsha, Hunan, China
- College of Computer Science and Engineering, Jishou University, Jishou, Hunan, China
- Key Laboratory of Medical Information Research, The Third Xiangya Hospital, Central South University, College of Hunan Province, Changsha, Hunan, China
- Clinical Research Center for Cardiovascular Intelligent Healthcare, Changsha, Hunan, China
| | - Wenzhao Xie
- Key Laboratory of Medical Information Research, The Third Xiangya Hospital, Central South University, College of Hunan Province, Changsha, Hunan, China
| | - Bolin Liao
- College of Computer Science and Engineering, Jishou University, Jishou, Hunan, China
| | - Chao Hu
- Big Data Institute, Central South University, Changsha 410083, China
| | - Lu Qin
- School of Life Sciences, Central South University, Changsha, Hunan, China
| | - Zhengzijin Yang
- School of Life Sciences, Central South University, Changsha, Hunan, China
| | - Huan Xiong
- School of Life Sciences, Central South University, Changsha, Hunan, China
| | - Yi Lyu
- School of Life Sciences, Central South University, Changsha, Hunan, China
| | - Yue Zhou
- School of Life Sciences, Central South University, Changsha, Hunan, China
| | - Aijing Luo
- The Second Xiangya Hospital of Central South University, No. 139, Renmin Road Central, Changsha, Hunan, China
- Key Laboratory of Medical Information Research, The Third Xiangya Hospital, Central South University, College of Hunan Province, Changsha, Hunan, China
- Clinical Research Center for Cardiovascular Intelligent Healthcare, Changsha, Hunan, China
| |
Collapse
|
14
|
Carusi A, Winter PD, Armstrong I, Ciravegna F, Kiely DG, Lawrie A, Lu H, Sabroe I, Swift A. Medical artificial intelligence is as much social as it is technological. NAT MACH INTELL 2023. [DOI: 10.1038/s42256-022-00603-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
|
15
|
Lu SC, Swisher CL, Chung C, Jaffray D, Sidey-Gibbons C. On the importance of interpretable machine learning predictions to inform clinical decision making in oncology. Front Oncol 2023; 13:1129380. [PMID: 36925929 PMCID: PMC10013157 DOI: 10.3389/fonc.2023.1129380] [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: 12/21/2022] [Accepted: 02/14/2023] [Indexed: 03/04/2023] Open
Abstract
Machine learning-based tools are capable of guiding individualized clinical management and decision-making by providing predictions of a patient's future health state. Through their ability to model complex nonlinear relationships, ML algorithms can often outperform traditional statistical prediction approaches, but the use of nonlinear functions can mean that ML techniques may also be less interpretable than traditional statistical methodologies. While there are benefits of intrinsic interpretability, many model-agnostic approaches now exist and can provide insight into the way in which ML systems make decisions. In this paper, we describe how different algorithms can be interpreted and introduce some techniques for interpreting complex nonlinear algorithms.
Collapse
Affiliation(s)
- Sheng-Chieh Lu
- Section of Patient-Centered Analytics, Division of Internal Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Christine L Swisher
- The Ronin Project, San Mateo, CA, United States.,The Lawrence J. Ellison Institute for Transformative Medicine, Los Angeles, CA, United States
| | - Caroline Chung
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.,Institute for Data Science in Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - David Jaffray
- Institute for Data Science in Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.,Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.,Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Chris Sidey-Gibbons
- Section of Patient-Centered Analytics, Division of Internal Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| |
Collapse
|
16
|
Shaikh AK, Alhashmi SM, Khalique N, Khedr AM, Raahemifar K, Bukhari S. Bibliometric analysis on the adoption of artificial intelligence applications in the e-health sector. Digit Health 2023; 9:20552076221149296. [PMID: 36683951 PMCID: PMC9850136 DOI: 10.1177/20552076221149296] [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: 07/09/2022] [Accepted: 12/18/2022] [Indexed: 01/19/2023] Open
Abstract
Artificial Intelligent (AI) applications in e-health have evolved considerably in the last 25 years. To track the current research progress in this field, there is a need to analyze the most recent trend of adopting AI applications in e-health. This bibliometric analysis study covers AI applications in e-health. It differs from the existing literature review as the journal articles are obtained from the Scopus database from its beginning to late 2021 (25 years), which depicts the most recent trend of AI in e-health. The bibliometric analysis is employed to find the statistical and quantitative analysis of available literature of a specific field of study for a particular period. An extensive global literature review is performed to identify the significant research area, authors, or their relationship through published articles. It also provides the researchers with an overview of the work evolution of specific research fields. The study's main contribution highlights the essential authors, journals, institutes, keywords, and states in developing the AI field in e-health.
Collapse
Affiliation(s)
| | - Saadat M Alhashmi
- Department of Information Systems, College of Computing and
Informatics, University of
Sharjah, Sharjah, United Arab
Emirates
| | - Nadia Khalique
- College of
Economics and Political Science, Sultan Qaboos
University, Muscat, Oman
| | - Ahmed M. Khedr
- Department of Information Systems, College of Computing and
Informatics, University of
Sharjah, Sharjah, United Arab
Emirates
| | | | - Sadaf Bukhari
- Beijing
Institute of Technology, Beijing, Beijing,
China
| |
Collapse
|
17
|
Liu CF, Hung CM, Ko SC, Cheng KC, Chao CM, Sung MI, Hsing SC, Wang JJ, Chen CJ, Lai CC, Chen CM, Chiu CC. An artificial intelligence system to predict the optimal timing for mechanical ventilation weaning for intensive care unit patients: A two-stage prediction approach. Front Med (Lausanne) 2022; 9:935366. [PMID: 36465940 PMCID: PMC9715756 DOI: 10.3389/fmed.2022.935366] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 10/11/2022] [Indexed: 11/03/2023] Open
Abstract
Background For the intensivists, accurate assessment of the ideal timing for successful weaning from the mechanical ventilation (MV) in the intensive care unit (ICU) is very challenging. Purpose Using artificial intelligence (AI) approach to build two-stage predictive models, namely, the try-weaning stage and weaning MV stage to determine the optimal timing of weaning from MV for ICU intubated patients, and implement into practice for assisting clinical decision making. Methods AI and machine learning (ML) technologies were used to establish the predictive models in the stages. Each stage comprised 11 prediction time points with 11 prediction models. Twenty-five features were used for the first-stage models while 20 features were used for the second-stage models. The optimal models for each time point were selected for further practical implementation in a digital dashboard style. Seven machine learning algorithms including Logistic Regression (LR), Random Forest (RF), Support Vector Machines (SVM), K Nearest Neighbor (KNN), lightGBM, XGBoost, and Multilayer Perception (MLP) were used. The electronic medical records of the intubated ICU patients of Chi Mei Medical Center (CMMC) from 2016 to 2019 were included for modeling. Models with the highest area under the receiver operating characteristic curve (AUC) were regarded as optimal models and used to develop the prediction system accordingly. Results A total of 5,873 cases were included in machine learning modeling for Stage 1 with the AUCs of optimal models ranging from 0.843 to 0.953. Further, 4,172 cases were included for Stage 2 with the AUCs of optimal models ranging from 0.889 to 0.944. A prediction system (dashboard) with the optimal models of the two stages was developed and deployed in the ICU setting. Respiratory care members expressed high recognition of the AI dashboard assisting ventilator weaning decisions. Also, the impact analysis of with- and without-AI assistance revealed that our AI models could shorten the patients' intubation time by 21 hours, besides gaining the benefit of substantial consistency between these two decision-making strategies. Conclusion We noticed that the two-stage AI prediction models could effectively and precisely predict the optimal timing to wean intubated patients in the ICU from ventilator use. This could reduce patient discomfort, improve medical quality, and lower medical costs. This AI-assisted prediction system is beneficial for clinicians to cope with a high demand for ventilators during the COVID-19 pandemic.
Collapse
Affiliation(s)
- Chung-Feng Liu
- Department of Medical Research, Chi Mei Medical Center, Tainan, Taiwan
| | - Chao-Ming Hung
- Department of General Surgery, E-Da Cancer Hospital, Kaohsiung, Taiwan
- College of Medicine, I-Shou University, Kaohsiung, Taiwan
| | - Shian-Chin Ko
- Department of Respiratory Therapy, Chi Mei Medical Center, Tainan, Taiwan
| | - Kuo-Chen Cheng
- Department of Internal Medicine, Chi Mei Medical Center, Tainan, Taiwan
| | - Chien-Ming Chao
- Department of Intensive Care Medicine, Chi Mei Medical Center, Liouying, Taiwan
- Department of Dental Laboratory Technology, Min-Hwei College of Health Care Management, Liouying, Taiwan
| | - Mei-I Sung
- Department of Respiratory Therapy, Chi Mei Medical Center, Tainan, Taiwan
| | - Shu-Chen Hsing
- Department of Respiratory Therapy, Chi Mei Medical Center, Tainan, Taiwan
| | - Jhi-Joung Wang
- Department of Anesthesiology, Chi Mei Medical Center, Tainan, Taiwan
- Department of Anesthesiology, National Defense Medical Center, Taipei, Taiwan
| | - Chia-Jung Chen
- Department of Information Systems, Chi Mei Medical Center, Tainan, Taiwan
| | - Chih-Cheng Lai
- Division of Hospital Medicine, Department of Internal Medicine, Chi Mei Medical Center, Tainan, Taiwan
| | - Chin-Ming Chen
- Department of Intensive Care Medicine, Chi Mei Medical Center, Tainan, Taiwan
| | - Chong-Chi Chiu
- Department of General Surgery, E-Da Cancer Hospital, Kaohsiung, Taiwan
- School of Medicine, College of Medicine, I-Shou University, Kaohsiung, Taiwan
- Department of Medical Education and Research, E-Da Cancer Hospital, Kaohsiung, Taiwan
- Department of General Surgery, Chi Mei Medical Center, Tainan, Taiwan
| |
Collapse
|
18
|
Almásy MG, Hörömpő A, Kiss D, Kertész G. A review on modeling tumor dynamics and agent reward functions in reinforcement learning based therapy optimization. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-212351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Revolutionary changes of deep reinforcement learning are leading to high-performing intelligent solutions in multiple fields, including healthcare. At the moment, chemotherapy and radiotherapy are common types of treatments for cancer, however, both therapies are usually radical procedures with undesirable side effects. There is an increasing number of evidence that patient-based optimal schedule has a significant impact in increasing efficiency and survival, and reducing side effects during both therapies. To apply artificial intelligence in therapy optimization, an adequate model of tumor growth incorporating the effect of the treatment is mandatory. A method on training a controller for dosage and scheduling, reinforcement learning can be applied, where a well-chosen agent rewarding function is key to achieve optimal behavior. In this survey paper, some selected tumor growth models, reinforcement learning based solutions and especially agent reward functions are reviewed and compared, providing a summary on state of the art approaches.
Collapse
Affiliation(s)
| | - András Hörömpő
- Obuda University John von Neumann Faculty of Informatics, Budapest, Hungary
| | - Dániel Kiss
- Obuda University John von Neumann Faculty of Informatics, Budapest, Hungary
| | - Gábor Kertész
- Obuda University John von Neumann Faculty of Informatics, Budapest, Hungary
- Institute for Computer Science and Control (SZTAKI), Eötvös Loránd Research Network (ELKH), Budapest, Hungary
| |
Collapse
|
19
|
Visuña L, Yang D, Garcia-Blas J, Carretero J. Computer-aided diagnostic for classifying chest X-ray images using deep ensemble learning. BMC Med Imaging 2022; 22:178. [PMID: 36243705 PMCID: PMC9568999 DOI: 10.1186/s12880-022-00904-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 09/05/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Nowadays doctors and radiologists are overwhelmed with a huge amount of work. This led to the effort to design different Computer-Aided Diagnosis systems (CAD system), with the aim of accomplishing a faster and more accurate diagnosis. The current development of deep learning is a big opportunity for the development of new CADs. In this paper, we propose a novel architecture for a convolutional neural network (CNN) ensemble for classifying chest X-ray (CRX) images into four classes: viral Pneumonia, Tuberculosis, COVID-19, and Healthy. Although Computed tomography (CT) is the best way to detect and diagnoses pulmonary issues, CT is more expensive than CRX. Furthermore, CRX is commonly the first step in the diagnosis, so it's very important to be accurate in the early stages of diagnosis and treatment. RESULTS We applied the transfer learning technique and data augmentation to all CNNs for obtaining better performance. We have designed and evaluated two different CNN-ensembles: Stacking and Voting. This system is ready to be applied in a CAD system to automated diagnosis such a second or previous opinion before the doctors or radiology's. Our results show a great improvement, 99% accuracy of the Stacking Ensemble and 98% of accuracy for the the Voting Ensemble. CONCLUSIONS To minimize missclassifications, we included six different base CNN models in our architecture (VGG16, VGG19, InceptionV3, ResNet101V2, DenseNet121 and CheXnet) and it could be extended to any number as well as we expect extend the number of diseases to detected. The proposed method has been validated using a large dataset created by mixing several public datasets with different image sizes and quality. As we demonstrate in the evaluation carried out, we reach better results and generalization compared with previous works. In addition, we make a first approach to explainable deep learning with the objective of providing professionals more information that may be valuable when evaluating CRXs.
Collapse
Affiliation(s)
- Lara Visuña
- Department of Computer Science and Engineering, University Carlos III, Madrid, Spain
| | - Dandi Yang
- Beijing Electro-Mechanical Engineering Institute, Beijing, China
| | - Javier Garcia-Blas
- Department of Computer Science and Engineering, University Carlos III, Madrid, Spain
| | - Jesus Carretero
- Department of Computer Science and Engineering, University Carlos III, Madrid, Spain
| |
Collapse
|
20
|
Romero D, Blanco-Almazán D, Groenendaal W, Lijnen L, Smeets C, Ruttens D, Catthoor F, Jané R. Predicting 6-minute walking test outcomes in patients with chronic obstructive pulmonary disease without physical performance measures. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 225:107020. [PMID: 35905697 DOI: 10.1016/j.cmpb.2022.107020] [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: 11/30/2021] [Revised: 06/20/2022] [Accepted: 07/10/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE Chronic obstructive pulmonary disease (COPD) requires a multifactorial assessment, evaluating the airflow limitation and symptoms of the patients. The 6-min walk test (6MWT) is commonly used to evaluate the functional exercise capacity in these patients. This study aims to propose a novel predictive model of the major 6MWT outcomes for COPD assessment, without physical performance measurements. METHODS Cardiopulmonary and clinical parameters were obtained from fifty COPD patients. These parameters were used as inputs of a Bayesian network (BN), which integrated three multivariate models including the 6-min walking distance (6MWD), the maximum HR (HRmax) after the walking, and the HR decay 3 min after (HRR3). The use of BN allows the assessment of the patients' status by predicting the 6MWT outcomes, but also inferring disease severity parameters based on actual patient's 6MWT outcomes. RESULTS Firstly, the correlation obtained between the estimated and actual 6MWT measures was strong (R = 0.84, MAPE = 8.10% for HRmax) and moderate (R = 0.58, MAPE = 15.43% for 6MWD and R = 0.58, MAPE = 32.49% for HRR3), improving the classical methods to estimate 6MWD. Secondly, the classification of disease severity showed an accuracy of 78.3% using three severity groups, which increased up to 84.4% for two defined severity groups. CONCLUSIONS We propose a powerful two-way assessment tool for COPD patients, capable of predicting 6MWT outcomes without the need for an actual walking exercise. This model-based tool opens the way to implement a continuous monitoring system for COPD patients at home and to provide more personalized care.
Collapse
Affiliation(s)
- Daniel Romero
- Universitat Politecnica de Catalunya · BarcelonaTech (UPC), Barcelona 08019, Spain; Institute for Bioengineering of Catalonia (IBEC-BIST), Barcelona 08019, Spain; Biomedical Research Networking Center of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid 28029, Spain.
| | - Dolores Blanco-Almazán
- Universitat Politecnica de Catalunya · BarcelonaTech (UPC), Barcelona 08019, Spain; Institute for Bioengineering of Catalonia (IBEC-BIST), Barcelona 08019, Spain; Biomedical Research Networking Center of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid 28029, Spain
| | | | | | | | | | | | - Raimon Jané
- Universitat Politecnica de Catalunya · BarcelonaTech (UPC), Barcelona 08019, Spain; Institute for Bioengineering of Catalonia (IBEC-BIST), Barcelona 08019, Spain; Biomedical Research Networking Center of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid 28029, Spain
| |
Collapse
|
21
|
Vitacca M, Giardini A, Gazzi L, Vitacca M. Hidden biases in clinical decision-making: potential solutions, challenges, and perspectives. Monaldi Arch Chest Dis 2022; 93. [PMID: 36069639 DOI: 10.4081/monaldi.2022.2339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 09/02/2022] [Indexed: 11/22/2022] Open
Abstract
Every day, we must make decisions that range from simple and risk-free to difficult and risky. Our cognitive sources' limitations, as well as the need for speed, can frequently impair the quality and accuracy of our reasoning processes. Indeed, cognitive shortcuts lead us to solutions that are sufficiently satisfying to allow us to make quick decisions. Unfortunately, heuristics frequently misguide us, and we fall victim to biases and systematic distortions of our perceptions and judgments. Because suboptimal diagnostic reasoning processes can have dramatic consequences, the clinical setting is an ideal setting for developing targeted interventions to reduce the rates and magnitude of biases. There are several approaches to bias mitigation, some of which may be impractical. Furthermore, advances in information technology have given us powerful tools for addressing and preventing errors in health care. Recognizing and accepting the role of biases is only the first and unavoidable step toward any effective intervention proposal. As a result, our narrative review aims to present some insights on this contentious topic based on both medical and psychological literature.
Collapse
|
22
|
Denecke K, Baudoin CR. A Review of Artificial Intelligence and Robotics in Transformed Health Ecosystems. Front Med (Lausanne) 2022; 9:795957. [PMID: 35872767 PMCID: PMC9299071 DOI: 10.3389/fmed.2022.795957] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 06/15/2022] [Indexed: 11/13/2022] Open
Abstract
Health care is shifting toward become proactive according to the concept of P5 medicine-a predictive, personalized, preventive, participatory and precision discipline. This patient-centered care heavily leverages the latest technologies of artificial intelligence (AI) and robotics that support diagnosis, decision making and treatment. In this paper, we present the role of AI and robotic systems in this evolution, including example use cases. We categorize systems along multiple dimensions such as the type of system, the degree of autonomy, the care setting where the systems are applied, and the application area. These technologies have already achieved notable results in the prediction of sepsis or cardiovascular risk, the monitoring of vital parameters in intensive care units, or in the form of home care robots. Still, while much research is conducted around AI and robotics in health care, adoption in real world care settings is still limited. To remove adoption barriers, we need to address issues such as safety, security, privacy and ethical principles; detect and eliminate bias that could result in harmful or unfair clinical decisions; and build trust in and societal acceptance of AI.
Collapse
Affiliation(s)
- Kerstin Denecke
- Institute for Medical Information, Bern University of Applied Sciences, Bern, Switzerland
| | | |
Collapse
|
23
|
Chen Y, Lee JKY, Kwong G, Pow EHN, Tsoi JKH. Morphology and fracture behavior of lithium disilicate dental crowns designed by human and knowledge-based AI. J Mech Behav Biomed Mater 2022; 131:105256. [DOI: 10.1016/j.jmbbm.2022.105256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 04/20/2022] [Accepted: 04/25/2022] [Indexed: 11/25/2022]
|
24
|
Sharma M, Savage C, Nair M, Larsson I, Svedberg P, Nygren JM. Artificial Intelligence Applications in Health Care Practice: A Scoping Review (Preprint). J Med Internet Res 2022; 24:e40238. [PMID: 36197712 PMCID: PMC9582911 DOI: 10.2196/40238] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 08/19/2022] [Accepted: 08/30/2022] [Indexed: 11/25/2022] Open
Abstract
Background Artificial intelligence (AI) is often heralded as a potential disruptor that will transform the practice of medicine. The amount of data collected and available in health care, coupled with advances in computational power, has contributed to advances in AI and an exponential growth of publications. However, the development of AI applications does not guarantee their adoption into routine practice. There is a risk that despite the resources invested, benefits for patients, staff, and society will not be realized if AI implementation is not better understood. Objective The aim of this study was to explore how the implementation of AI in health care practice has been described and researched in the literature by answering 3 questions: What are the characteristics of research on implementation of AI in practice? What types and applications of AI systems are described? What characteristics of the implementation process for AI systems are discernible? Methods A scoping review was conducted of MEDLINE (PubMed), Scopus, Web of Science, CINAHL, and PsycINFO databases to identify empirical studies of AI implementation in health care since 2011, in addition to snowball sampling of selected reference lists. Using Rayyan software, we screened titles and abstracts and selected full-text articles. Data from the included articles were charted and summarized. Results Of the 9218 records retrieved, 45 (0.49%) articles were included. The articles cover diverse clinical settings and disciplines; most (32/45, 71%) were published recently, were from high-income countries (33/45, 73%), and were intended for care providers (25/45, 56%). AI systems are predominantly intended for clinical care, particularly clinical care pertaining to patient-provider encounters. More than half (24/45, 53%) possess no action autonomy but rather support human decision-making. The focus of most research was on establishing the effectiveness of interventions (16/45, 35%) or related to technical and computational aspects of AI systems (11/45, 24%). Focus on the specifics of implementation processes does not yet seem to be a priority in research, and the use of frameworks to guide implementation is rare. Conclusions Our current empirical knowledge derives from implementations of AI systems with low action autonomy and approaches common to implementations of other types of information systems. To develop a specific and empirically based implementation framework, further research is needed on the more disruptive types of AI systems being implemented in routine care and on aspects unique to AI implementation in health care, such as building trust, addressing transparency issues, developing explainable and interpretable solutions, and addressing ethical concerns around privacy and data protection.
Collapse
Affiliation(s)
- Malvika Sharma
- Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, Medical Management Centre, Stockholm, Sweden
| | - Carl Savage
- Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, Medical Management Centre, Stockholm, Sweden
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Monika Nair
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Ingrid Larsson
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Petra Svedberg
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Jens M Nygren
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| |
Collapse
|
25
|
Gomez Rossi J, Rojas-Perilla N, Krois J, Schwendicke F. Cost-effectiveness of Artificial Intelligence as a Decision-Support System Applied to the Detection and Grading of Melanoma, Dental Caries, and Diabetic Retinopathy. JAMA Netw Open 2022; 5:e220269. [PMID: 35289862 PMCID: PMC8924723 DOI: 10.1001/jamanetworkopen.2022.0269] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
OBJECTIVE To assess the cost-effectiveness of artificial intelligence (AI) for supporting clinicians in detecting and grading diseases in dermatology, dentistry, and ophthalmology. IMPORTANCE AI has been referred to as a facilitator for more precise, personalized, and safer health care, and AI algorithms have been reported to have diagnostic accuracies at or above the average physician in dermatology, dentistry, and ophthalmology. DESIGN, SETTING, AND PARTICIPANTS This economic evaluation analyzed data from 3 Markov models used in previous cost-effectiveness studies that were adapted to compare AI vs standard of care to detect melanoma on skin photographs, dental caries on radiographs, and diabetic retinopathy on retina fundus imaging. The general US and German population aged 50 and 12 years, respectively, as well as individuals with diabetes in Brazil aged 40 years were modeled over their lifetime. Monte Carlo microsimulations and sensitivity analyses were used to capture lifetime efficacy and costs. An annual cycle length was chosen. Data were analyzed between February 2021 and August 2021. EXPOSURE AI vs standard of care. MAIN OUTCOMES AND MEASURES Association of AI with tooth retention-years for dentistry and quality-adjusted life-years (QALYs) for individuals in dermatology and ophthalmology; diagnostic costs. RESULTS In 1000 microsimulations with 1000 random samples, AI as a diagnostic-support system showed limited cost-savings and gains in tooth retention-years and QALYs. In dermatology, AI showed mean costs of $750 (95% CI, $608-$970) and was associated with 86.5 QALYs (95% CI, 84.9-87.9 QALYs), while the control showed higher costs $759 (95% CI, $618-$970) with similar QALY outcome. In dentistry, AI accumulated costs of €320 (95% CI, €299-€341) (purchasing power parity [PPP] conversion, $429 [95% CI, $400-$458]) with 62.4 years per tooth retention (95% CI, 60.7-65.1 years). The control was associated with higher cost, €342 (95% CI, €318-€368) (PPP, $458; 95% CI, $426-$493) and fewer tooth retention-years (60.9 years; 95% CI, 60.5-63.1 years). In ophthalmology, AI accrued costs of R $1321 (95% CI, R $1283-R $1364) (PPP, $559; 95% CI, $543-$577) at 8.4 QALYs (95% CI, 8.0-8.7 QALYs), while the control was less expensive (R $1260; 95% CI, R $1222-R $1303) (PPP, $533; 95% CI, $517-$551) and associated with similar QALYs. Dominance in favor of AI was dependent on small differences in the fee paid for the service and the treatment assumed after diagnosis. The fee paid for AI was a factor in patient preferences in cost-effectiveness between strategies. CONCLUSIONS AND RELEVANCE The findings of this study suggest that marginal improvements in diagnostic accuracy when using AI may translate into a marginal improvement in outcomes. The current evidence supporting AI as decision support from a cost-effectiveness perspective is limited; AI should be evaluated on a case-specific basis to capture not only differences in costs and payment mechanisms but also treatment after diagnosis.
Collapse
Affiliation(s)
- Jesus Gomez Rossi
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité–Universitätsmedizin Berlin, Berlin, Germany
| | - Natalia Rojas-Perilla
- Department of Economics, Freie Universität Berlin, Germany
- Department of Analytics in the Digital Era, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Joachim Krois
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité–Universitätsmedizin Berlin, Berlin, Germany
| | - Falk Schwendicke
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité–Universitätsmedizin Berlin, Berlin, Germany
| |
Collapse
|
26
|
Hung CM, Shi HY, Lee PH, Chang CS, Rau KM, Lee HM, Tseng CH, Pei SN, Tsai KJ, Chiu CC. Potential and role of artificial intelligence in current medical healthcare. Artif Intell Cancer 2022; 3:1-10. [DOI: 10.35713/aic.v3.i1.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 12/31/2021] [Accepted: 02/20/2022] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) is defined as the digital computer or computer-controlled robot's ability to mimic intelligent conduct and crucial thinking commonly associated with intelligent beings. The application of AI technology and machine learning in medicine have allowed medical practitioners to provide patients with better quality of services; and current advancements have led to a dramatic change in the healthcare system. However, many efficient applications are still in their initial stages, which need further evaluations to improve and develop these applications. Clinicians must recognize and acclimate themselves with the developments in AI technology to improve their delivery of healthcare services; but for this to be possible, a significant revision of medical education is needed to provide future leaders with the required competencies. This article reviews the potential and limitations of AI in healthcare, as well as the current medical application trends including healthcare administration, clinical decision assistance, patient health monitoring, healthcare resource allocation, medical research, and public health policy development. Also, future possibilities for further clinical and scientific practice were also summarized.
Collapse
Affiliation(s)
- Chao-Ming Hung
- Department of General Surgery, E-Da Cancer Hospital, Kaohsiung 82445, Taiwan
- College of Medicine, I-Shou University, Kaohsiung 82445, Taiwan
| | - Hon-Yi Shi
- Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
- Department of Business Management, National Sun Yat-Sen University, Kaohsiung 80420, Taiwan
- Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung 80708, Taiwan
- Department of Medical Research, China Medical University Hospital, China Medical University, Taichung 40402, Taiwan
| | - Po-Huang Lee
- College of Medicine, I-Shou University, Kaohsiung 82445, Taiwan
- Department of Surgery, E-Da Hospital, Kaohsiung 82445, Taiwan
| | - Chao-Sung Chang
- Department of Hematology & Oncology, E-Da Cancer Hospital, Kaohsiung 82445, Taiwan
- School of Medicine for International Students, College of Medicine, I-Shou University, Kaohsiung 82445, Taiwan
| | - Kun-Ming Rau
- Department of Hematology & Oncology, E-Da Cancer Hospital, Kaohsiung 82445, Taiwan
- School of Medicine, College of Medicine, I-Shou University, Kaohsiung 82445, Taiwan
| | - Hui-Ming Lee
- Department of General Surgery, E-Da Cancer Hospital, Kaohsiung 82445, Taiwan
- College of Medicine, I-Shou University, Kaohsiung 82445, Taiwan
| | - Cheng-Hao Tseng
- School of Medicine, College of Medicine, I-Shou University, Kaohsiung 82445, Taiwan
- Department of Gastroenterology and Hepatology, E-Da Cancer Hospital, Kaohsiung 82445, Taiwan
- Department of Gastroenterology and Hepatology, E-Da Hospital, Kaohsiung 82445, Taiwan
| | - Sung-Nan Pei
- Department of Hematology & Oncology, E-Da Cancer Hospital, Kaohsiung 82445, Taiwan
- School of Medicine, College of Medicine, I-Shou University, Kaohsiung 82445, Taiwan
| | - Kuen-Jang Tsai
- Department of General Surgery, E-Da Cancer Hospital, Kaohsiung 82445, Taiwan
| | - Chong-Chi Chiu
- Department of General Surgery, E-Da Cancer Hospital, Kaohsiung 82445, Taiwan
- School of Medicine, College of Medicine, I-Shou University, Kaohsiung 82445, Taiwan
- Department of Medical Education and Research, E-Da Cancer Hospital, Kaohsiung 82445, Taiwan
| |
Collapse
|
27
|
A Temporal Case-Based Reasoning Platform Relying on a Fuzzy Vector Spaces Object-Oriented Model and a Method to Design Knowledge Bases and Decision Support Systems in Multiple Domains. ALGORITHMS 2022. [DOI: 10.3390/a15020066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Knowledge bases in complex domains must take into account many attributes describing numerous objects that are themselves components of complex objects. Temporal case-based reasoning (TCBR) requires comparing the structural evolution of component objects and their states (attribute values) at different levels of granularity. This paper provides some significant contributions to computer science. It extends a fuzzy vector space object-oriented model and method (FVSOOMM) to present a new platform and a method guideline capable of designing objects and attributes that represent timepoint knowledge objects. It shows how temporal case-based reasoning can use distances between temporal fuzzy vector functions to compare these knowledge objects’ evolution. It describes examples of interfaces that have been implemented on this new platform. These include an expert’s interface that describes a knowledge class diagram; a practitioner’s interface that instantiates domain objects and their attribute constraints; and an end-user interface to input attribute values of the real cases stored in a domain case database. This paper illustrates resultant knowledge bases in different domains, with examples of pulmonary embolism diagnosis in medicine and decision making in French municipal territorial recomposition. The paper concludes with the current limitations of the proposed model, its future perspectives and possible platform enhancements.
Collapse
|
28
|
Furtner D, Shinde SP, Singh M, Wong CH, Setia S. Digital Transformation in Medical Affairs Sparked by the Pandemic: Insights and Learnings from COVID-19 Era and Beyond. Pharmaceut Med 2022; 36:1-10. [PMID: 34970723 PMCID: PMC8718376 DOI: 10.1007/s40290-021-00412-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/28/2021] [Indexed: 11/12/2022]
Abstract
A number of developments, including increasing regulatory and compliance scrutiny, increased transparency expectations, an increasingly vocal patient, patient centricity and greater requirements for real-world evidence, have driven the growth and importance of medical affairs as a trusted, science-driven partner over the past decade. The healthcare environment is shifting towards a digital, data-driven and payor-focused model. Likewise, medical affairs as a function within the pharmaceutical industry has become more "patient-centric" with strategic engagements embracing payers and patients apart from clinicians. The pandemic has impacted the healthcare industry as well as the function of medical affairs in numerous ways and has brought new challenges and demands to tackle. There is indeed a silver lining due to intense digital transformation within this crisis. The emerging digital innovation and new technologies in healthcare, medical education and virtual communications are likely to stay and advance further. In this review, we discuss how the digital transformation sparked by the pandemic has impacted the medical affairs function in pharmaceuticals and provide further insights and learnings from the COVID-19 era and beyond. Based on the learning and insights, digital innovation in three key strategic imperatives of medical affairs-HCP engagement, external partnerships and data generation will enable medical affairs to become future-fit as a strategic leadership function.
Collapse
Affiliation(s)
- Daniel Furtner
- Executive Office, Transform Medical Communications Limited, 184 Glasgow Street, Wanganui, 4500, New Zealand.
| | - Salil Prakash Shinde
- Regional Medical Affairs, Pfizer Corporation Hong Kong Limited, 21st Floor, Kerry Center, 683 King's Road, Quarry Bay, Hong Kong
| | - Manmohan Singh
- Regional Medical Affairs, Pfizer Corporation Hong Kong Limited, 21st Floor, Kerry Center, 683 King's Road, Quarry Bay, Hong Kong
| | - Chew Hooi Wong
- Regional Medical Affairs, Pfizer Private Limited, 80 Pasir Panjang Road, #16-81/82, Mapletree Business City, Singapore, 117372, Singapore
| | - Sajita Setia
- Executive Office, Transform Medical Communications Limited, 184 Glasgow Street, Wanganui, 4500, New Zealand
| |
Collapse
|
29
|
Three-stage intelligent support of clinical decision making for higher trust, validity, and explainability. J Biomed Inform 2022; 127:104013. [DOI: 10.1016/j.jbi.2022.104013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 01/03/2022] [Accepted: 02/02/2022] [Indexed: 01/02/2023]
|
30
|
Ahne A, Fagherazzi G, Tannier X, Czernichow T, Orchard F. Improving Diabetes-Related Biomedical Literature Exploration in the Clinical Decision-making Process via Interactive Classification and Topic Discovery: Methodology Development Study. J Med Internet Res 2022; 24:e27434. [PMID: 35040795 PMCID: PMC8808347 DOI: 10.2196/27434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 04/06/2021] [Accepted: 11/10/2021] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND The amount of available textual health data such as scientific and biomedical literature is constantly growing and becoming more and more challenging for health professionals to properly summarize those data and practice evidence-based clinical decision making. Moreover, the exploration of unstructured health text data is challenging for professionals without computer science knowledge due to limited time, resources, and skills. Current tools to explore text data lack ease of use, require high computational efforts, and incorporate domain knowledge and focus on topics of interest with difficulty. OBJECTIVE We developed a methodology able to explore and target topics of interest via an interactive user interface for health professionals with limited computer science knowledge. We aim to reach near state-of-the-art performance while reducing memory consumption, increasing scalability, and minimizing user interaction effort to improve the clinical decision-making process. The performance was evaluated on diabetes-related abstracts from PubMed. METHODS The methodology consists of 4 parts: (1) a novel interpretable hierarchical clustering of documents where each node is defined by headwords (words that best represent the documents in the node), (2) an efficient classification system to target topics, (3) minimized user interaction effort through active learning, and (4) a visual user interface. We evaluated our approach on 50,911 diabetes-related abstracts providing a hierarchical Medical Subject Headings (MeSH) structure, a unique identifier for a topic. Hierarchical clustering performance was compared against the implementation in the machine learning library scikit-learn. On a subset of 2000 randomly chosen diabetes abstracts, our active learning strategy was compared against 3 other strategies: random selection of training instances, uncertainty sampling that chooses instances about which the model is most uncertain, and an expected gradient length strategy based on convolutional neural networks (CNNs). RESULTS For the hierarchical clustering performance, we achieved an F1 score of 0.73 compared to 0.76 achieved by scikit-learn. Concerning active learning performance, after 200 chosen training samples based on these strategies, the weighted F1 score of all MeSH codes resulted in a satisfying 0.62 F1 score using our approach, 0.61 using the uncertainty strategy, 0.63 using the CNN, and 0.45 using the random strategy. Moreover, our methodology showed a constant low memory use with increased number of documents. CONCLUSIONS We proposed an easy-to-use tool for health professionals with limited computer science knowledge who combine their domain knowledge with topic exploration and target specific topics of interest while improving transparency. Furthermore, our approach is memory efficient and highly parallelizable, making it interesting for large Big Data sets. This approach can be used by health professionals to gain deep insights into biomedical literature to ultimately improve the evidence-based clinical decision making process.
Collapse
Affiliation(s)
- Adrian Ahne
- Exposome and Heredity team, Center of Epidemiology and Population Health, Hospital Gustave Roussy, Inserm, Paris-Saclay University, Villejuif, France
- Epiconcept Company, Paris, France
| | - Guy Fagherazzi
- Deep Digital Phenotyping Research Unit, Department of Population Health, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Xavier Tannier
- Laboratoire d'Informatique Medicale et d'Ingenierie des Connaissances pour la e-Sante, Limics, Inserm, University Sorbonne Paris Nord, Sorbonne University, Paris, France
| | | | | |
Collapse
|
31
|
Deep learning for anatomical interpretation of video bronchoscopy images. Sci Rep 2021; 11:23765. [PMID: 34887497 PMCID: PMC8660867 DOI: 10.1038/s41598-021-03219-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 11/24/2021] [Indexed: 11/25/2022] Open
Abstract
Anesthesiologists commonly use video bronchoscopy to facilitate intubation or confirm the location of the endotracheal tube; however, depth and orientation in the bronchial tree can often be confused because anesthesiologists cannot trace the airway from the oropharynx when it is performed using an endotracheal tube. Moreover, the decubitus position is often used in certain surgeries. Although it occurs rarely, the misinterpretation of tube location can cause accidental extubation or endobronchial intubation, which can lead to hyperinflation. Thus, video bronchoscopy with a decision supporting system using artificial intelligence would be useful in the anesthesiologic process. In this study, we aimed to develop an artificial intelligence model robust to rotation and covering using video bronchoscopy images. We collected video bronchoscopic images from an institutional database. Collected images were automatically labeled by an optical character recognition engine as the carina and left/right main bronchus. Except 180 images for the evaluation dataset, 80% were randomly allocated to the training dataset. The remaining images were assigned to the validation and test datasets in a 7:3 ratio. Random image rotation and circular cropping were applied. Ten kinds of pretrained models with < 25 million parameters were trained on the training and validation datasets. The model showing the best prediction accuracy for the test dataset was selected as the final model. Six human experts reviewed the evaluation dataset for the inference of anatomical locations to compare its performance with that of the final model. In the experiments, 8688 images were prepared and assigned to the evaluation (180), training (6806), validation (1191), and test (511) datasets. The EfficientNetB1 model showed the highest accuracy (0.86) and was selected as the final model. For the evaluation dataset, the final model showed better performance (accuracy, 0.84) than almost all human experts (0.38, 0.44, 0.51, 0.68, and 0.63), and only the most-experienced pulmonologist showed performance comparable (0.82) with that of the final model. The performance of human experts was generally proportional to their experiences. The performance difference between anesthesiologists and pulmonologists was marked in discrimination of the right main bronchus. Using bronchoscopic images, our model could distinguish anatomical locations among the carina and both main bronchi under random rotation and covering. The performance was comparable with that of the most-experienced human expert. This model can be a basis for designing a clinical decision support system with video bronchoscopy.
Collapse
|
32
|
|
33
|
van Baalen S, Boon M, Verhoef P. From clinical decision support to clinical reasoning support systems. J Eval Clin Pract 2021; 27:520-528. [PMID: 33554432 PMCID: PMC8248191 DOI: 10.1111/jep.13541] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 12/15/2020] [Accepted: 01/07/2021] [Indexed: 12/19/2022]
Abstract
Despite the great promises that artificial intelligence (AI) holds for health care, the uptake of such technologies into medical practice is slow. In this paper, we focus on the epistemological issues arising from the development and implementation of a class of AI for clinical practice, namely clinical decision support systems (CDSS). We will first provide an overview of the epistemic tasks of medical professionals, and then analyse which of these tasks can be supported by CDSS, while also explaining why some of them should remain the territory of human experts. Clinical decision making involves a reasoning process in which clinicians combine different types of information into a coherent and adequate 'picture of the patient' that enables them to draw explainable and justifiable conclusions for which they bear epistemological responsibility. Therefore, we suggest that it is more appropriate to think of a CDSS as clinical reasoning support systems (CRSS). Developing CRSS that support clinicians' reasoning process therefore requires that: (a) CRSSs are developed on the basis of relevant and well-processed data; and (b) the system facilitates an interaction with the clinician. Therefore, medical experts must collaborate closely with AI experts developing the CRSS. In addition, responsible use of an CRSS requires that the data generated by the CRSS is empirically justified through an empirical link with the individual patient. In practice, this means that the system indicates what factors contributed to arriving at an advice, allowing the user (clinician) to evaluate whether these factors are medically plausible and applicable to the patient. Finally, we defend that proper implementation of CRSS allows combining human and artificial intelligence into hybrid intelligence, were both perform clearly delineated and complementary empirical tasks. Whereas CRSSs can assist with statistical reasoning and finding patterns in complex data, it is the clinicians' task to interpret, integrate and contextualize.
Collapse
Affiliation(s)
| | - Mieke Boon
- Department of PhilosophyUniversity of TwenteEnschedeThe Netherlands
| | | |
Collapse
|
34
|
Tarumi S, Takeuchi W, Chalkidis G, Rodriguez-Loya S, Kuwata J, Flynn M, Turner KM, Sakaguchi FH, Weir C, Kramer H, Shields DE, Warner PB, Kukhareva P, Ban H, Kawamoto K. Leveraging Artificial Intelligence to Improve Chronic Disease Care: Methods and Application to Pharmacotherapy Decision Support for Type-2 Diabetes Mellitus. Methods Inf Med 2021; 60:e32-e43. [PMID: 33975376 PMCID: PMC8294941 DOI: 10.1055/s-0041-1728757] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 02/21/2021] [Indexed: 12/17/2022]
Abstract
OBJECTIVES Artificial intelligence (AI), including predictive analytics, has great potential to improve the care of common chronic conditions with high morbidity and mortality. However, there are still many challenges to achieving this vision. The goal of this project was to develop and apply methods for enhancing chronic disease care using AI. METHODS Using a dataset of 27,904 patients with diabetes, an analytical method was developed and validated for generating a treatment pathway graph which consists of models that predict the likelihood of alternate treatment strategies achieving care goals. An AI-driven clinical decision support system (CDSS) integrated with the electronic health record (EHR) was developed by encapsulating the prediction models in an OpenCDS Web service module and delivering the model outputs through a SMART on FHIR (Substitutable Medical Applications and Reusable Technologies on Fast Healthcare Interoperability Resources) web-based dashboard. This CDSS enables clinicians and patients to review relevant patient parameters, select treatment goals, and review alternate treatment strategies based on prediction results. RESULTS The proposed analytical method outperformed previous machine-learning algorithms on prediction accuracy. The CDSS was successfully integrated with the Epic EHR at the University of Utah. CONCLUSION A predictive analytics-based CDSS was developed and successfully integrated with the EHR through standards-based interoperability frameworks. The approach used could potentially be applied to many other chronic conditions to bring AI-driven CDSS to the point of care.
Collapse
Affiliation(s)
- Shinji Tarumi
- Department of Media Intelligent Processing Research, Center for Technology Innovation Artificial Intelligence, Hitachi Ltd., Kokubunji, Tokyo, Japan
| | - Wataru Takeuchi
- Department of Media Intelligent Processing Research, Center for Technology Innovation Artificial Intelligence, Hitachi Ltd., Kokubunji, Tokyo, Japan
| | - George Chalkidis
- Department of Media Intelligent Processing Research, Center for Technology Innovation Artificial Intelligence, Hitachi Ltd., Kokubunji, Tokyo, Japan
| | - Salvador Rodriguez-Loya
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States
| | - Junichi Kuwata
- Department of Product Design, Center for Social Innovation, Hitachi Ltd., Kokubunji, Tokyo, Japan
| | - Michael Flynn
- Departments of Internal Medicine and Pediatrics, University of Utah, Salt Lake City, Utah, United States
| | - Kyle M. Turner
- Department of Pharmacotherapy, University of Utah, Salt Lake City, Utah, United States
| | - Farrant H. Sakaguchi
- Department of Family and Preventive Medicine, University of Utah, Salt Lake City, Utah, United States
| | - Charlene Weir
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States
| | - Heidi Kramer
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States
| | - David E. Shields
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States
| | - Phillip B. Warner
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States
| | - Polina Kukhareva
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States
| | - Hideyuki Ban
- Department of Media Intelligent Processing Research, Center for Technology Innovation Artificial Intelligence, Hitachi Ltd., Kokubunji, Tokyo, Japan
| | - Kensaku Kawamoto
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States
| |
Collapse
|
35
|
Mehta N, Born K, Fine B. How artificial intelligence can help us 'Choose Wisely'. Bioelectron Med 2021; 7:5. [PMID: 33879255 PMCID: PMC8057918 DOI: 10.1186/s42234-021-00066-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Accepted: 03/24/2021] [Indexed: 11/24/2022] Open
Abstract
The overuse of low value medical tests and treatments drives costs and patient harm. Efforts to address overuse, such as Choosing Wisely campaigns, typically rely on passive implementation strategies- a form of low reliability system change. Embedding guidelines into clinical decision support (CDS) software is a higher leverage approach to provide ordering suggestions through an interface embedded within the clinical workflow. Growth in computing power is increasingly enabling artificial intelligence (AI) to augment such decision making tools. This article offers a roadmap of opportunities for AI-enabled CDS to reduce overuse, which are presented according to a patient’s journey of care.
Collapse
Affiliation(s)
- Nishila Mehta
- Temerty Faculty of Medicine, King's College Cir, Toronto, ON, M5S 1A8, Canada. .,Unity Health Toronto, 30 Bond Street, Toronto, Ontario, M5B 1W8, Canada.
| | - Karen Born
- Unity Health Toronto, 30 Bond Street, Toronto, Ontario, M5B 1W8, Canada.,Institute for Health Policy, Management and Evaluation, University of Toronto, 155 College St 4th Floor, Toronto, ON, M5T 3M6, Canada
| | - Benjamin Fine
- Temerty Faculty of Medicine, King's College Cir, Toronto, ON, M5S 1A8, Canada.,Department of Diagnostic Imaging and Institute for Better Health, Trillium Health Partners, 2200 Eglinton Ave W, Mississauga, ON, L5M 2N1, Canada.,WCH Institute for Health System Solutions and Virtual Care (WIHV), Women's College Hospital, 76 Grenville St, Toronto, ON, M5S 1B2, Canada
| |
Collapse
|
36
|
Kumar R, Khan FU, Sharma A, Aziz IB, Poddar NK. Recent Applications of Artificial Intelligence in detection of Gastrointestinal, Hepatic and Pancreatic Diseases. Curr Med Chem 2021; 29:66-85. [PMID: 33820515 DOI: 10.2174/0929867328666210405114938] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 02/25/2021] [Accepted: 03/06/2021] [Indexed: 11/22/2022]
Abstract
There is substantial progress in artificial intelligence (AI) algorithms and their medical sciences applications in the last two decades. AI-assisted programs have already been established for remotely health monitoring using sensors and smartphones. A variety of AI-based prediction models available for the gastrointestinal inflammatory, non-malignant diseases, and bowel bleeding using wireless capsule endoscopy, electronic medical records for hepatitis-associated fibrosis, pancreatic carcinoma using endoscopic ultrasounds. AI-based models may be of immense help for healthcare professionals in the identification, analysis, and decision support using endoscopic images to establish prognosis and risk assessment of patient's treatment using multiple factors. Although enough randomized clinical trials are warranted to establish the efficacy of AI-algorithms assisted and non-AI based treatments before approval of such techniques from medical regulatory authorities. In this article, available AI approaches and AI-based prediction models for detecting gastrointestinal, hepatic, and pancreatic diseases are reviewed. The limitation of AI techniques in such disease prognosis, risk assessment, and decision support are discussed.
Collapse
Affiliation(s)
- Rajnish Kumar
- Amity Institute of Biotechnology, Amity University Uttar Pradesh Lucknow Campus, Uttar Pradesh. India
| | - Farhat Ullah Khan
- Computer and Information Sciences Department, Universiti Teknologi Petronas, 32610, Seri Iskander, Perak. Malaysia
| | - Anju Sharma
- Department of Applied Science, Indian Institute of Information Technology, Allahabad, Uttar Pradesh. India
| | - Izzatdin Ba Aziz
- Computer and Information Sciences Department, Universiti Teknologi Petronas, 32610, Seri Iskander, Perak. Malaysia
| | | |
Collapse
|
37
|
Abstract
Hypertension remains the largest modifiable cause of mortality worldwide despite the availability of effective medications and sustained research efforts over the past 100 years. Hypertension requires transformative solutions that can help reduce the global burden of the disease. Artificial intelligence and machine learning, which have made a substantial impact on our everyday lives over the last decade may be the route to this transformation. However, artificial intelligence in health care is still in its nascent stages and realizing its potential requires numerous challenges to be overcome. In this review, we provide a clinician-centric perspective on artificial intelligence and machine learning as applied to medicine and hypertension. We focus on the main roadblocks impeding implementation of this technology in clinical care and describe efforts driving potential solutions. At the juncture, there is a critical requirement for clinical and scientific expertise to work in tandem with algorithmic innovation followed by rigorous validation and scrutiny to realize the promise of artificial intelligence-enabled health care for hypertension and other chronic diseases.
Collapse
Affiliation(s)
- Sandosh Padmanabhan
- BHF Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, University of Glasgow
| | - Tran Quoc Bao Tran
- BHF Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, University of Glasgow
| | - Anna F Dominiczak
- BHF Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, University of Glasgow
| |
Collapse
|
38
|
Ten Broeke A, Hulscher J, Heyning N, Kooi E, Chorus C. BAIT: A New Medical Decision Support Technology Based on Discrete Choice Theory. Med Decis Making 2021; 41:614-619. [PMID: 33783246 PMCID: PMC8191159 DOI: 10.1177/0272989x211001320] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
We present a novel way to codify medical expertise and to make it available to support medical decision making. Our approach is based on econometric techniques (known as conjoint analysis or discrete choice theory) developed to analyze and forecast consumer or patient behavior; we reconceptualize these techniques and put them to use to generate an explainable, tractable decision support system for medical experts. The approach works as follows: using choice experiments containing systematically composed hypothetical choice scenarios, we collect a set of expert decisions. Then we use those decisions to estimate the weights that experts implicitly assign to various decision factors. The resulting choice model is able to generate a probabilistic assessment for real-life decision situations, in combination with an explanation of which factors led to the assessment. The approach has several advantages, but also potential limitations, compared to rule-based methods and machine learning techniques. We illustrate the choice model approach to support medical decision making by applying it in the context of the difficult choice to proceed to surgery v. comfort care for a critically ill neonate.
Collapse
Affiliation(s)
| | - Jan Hulscher
- Department of Surgery, Division of Pediatric Surgery, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | | | - Elisabeth Kooi
- University of Groningen, University Medical Center Groningen, Beatrix Kinder Ziekenhuis, Division of Neonatology, Groningen, Netherlands
| | - Caspar Chorus
- Councyl, Delft, Netherlands.,Faculty Technology Policy and Management, Department of Engineering Systems and Services, Delft University of Technology, Delft, Netherlands
| |
Collapse
|
39
|
An intelligent multimodal medical diagnosis system based on patients’ medical questions and structured symptoms for telemedicine. INFORMATICS IN MEDICINE UNLOCKED 2021. [DOI: 10.1016/j.imu.2021.100513] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
|
40
|
Brugnara G, Neuberger U, Mahmutoglu MA, Foltyn M, Herweh C, Nagel S, Schönenberger S, Heiland S, Ulfert C, Ringleb PA, Bendszus M, Möhlenbruch MA, Pfaff JA, Vollmuth P. Multimodal Predictive Modeling of Endovascular Treatment Outcome for Acute Ischemic Stroke Using Machine-Learning. Stroke 2020; 51:3541-3551. [DOI: 10.1161/strokeaha.120.030287] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Background and Purpose:
This study assessed the predictive performance and relative importance of clinical, multimodal imaging, and angiographic characteristics for predicting the clinical outcome of endovascular treatment for acute ischemic stroke.
Methods:
A consecutive series of 246 patients with acute ischemic stroke and large vessel occlusion in the anterior circulation who underwent endovascular treatment between April 2014 and January 2018 was analyzed. Clinical, conventional imaging (electronic Alberta Stroke Program Early CT Score, acute ischemic volume, site of vessel occlusion, and collateral score), and advanced imaging characteristics (CT-perfusion with quantification of ischemic penumbra and infarct core volumes) before treatment as well as angiographic (interval groin puncture-recanalization, modified Thrombolysis in Cerebral Infarction score) and postinterventional clinical (National Institutes of Health Stroke Scale score after 24 hours) and imaging characteristics (electronic Alberta Stroke Program Early CT Score, final infarction volume after 18–36 hours) were assessed. The modified Rankin Scale (mRS) score at 90 days (mRS-90) was used to measure patient outcome (favorable outcome: mRS-90 ≤2 versus unfavorable outcome: mRS-90 >2). Machine-learning with gradient boosting classifiers was used to assess the performance and relative importance of the extracted characteristics for predicting mRS-90.
Results:
Baseline clinical and conventional imaging characteristics predicted mRS-90 with an area under the receiver operating characteristics curve of 0.740 (95% CI, 0.733–0.747) and an accuracy of 0.711 (95% CI, 0.705–0.717). Advanced imaging with CT-perfusion did not improved the predictive performance (area under the receiver operating characteristics curve, 0.747 [95% CI, 0.740–0.755]; accuracy, 0.720 [95% CI, 0.714–0.727];
P
=0.150). Further inclusion of angiographic and postinterventional characteristics significantly improved the predictive performance (area under the receiver operating characteristics curve, 0.856 [95% CI, 0.850–0.861]; accuracy, 0.804 [95% CI, 0.799–0.810];
P
<0.001). The most important parameters for predicting mRS 90 were National Institutes of Health Stroke Scale score after 24 hours (importance =100%), premorbid mRS score (importance =44%) and final infarction volume on postinterventional CT after 18 to 36 hours (importance =32%).
Conclusions:
Integrative assessment of clinical, multimodal imaging, and angiographic characteristics with machine-learning allowed to accurately predict the clinical outcome following endovascular treatment for acute ischemic stroke. Thereby, premorbid mRS was the most important clinical predictor for mRS-90, and the final infarction volume was the most important imaging predictor, while the extent of hemodynamic impairment on CT-perfusion before treatment had limited importance.
Collapse
Affiliation(s)
- Gianluca Brugnara
- Department of Neuroradiology (G.B., U.N., M.A.M., M.F., Ch.H., S.H., C.U., M.B., M.A.M., J.A.R.P., P.V.), Heidelberg University Hospital, Germany
| | - Ulf Neuberger
- Department of Neuroradiology (G.B., U.N., M.A.M., M.F., Ch.H., S.H., C.U., M.B., M.A.M., J.A.R.P., P.V.), Heidelberg University Hospital, Germany
| | - Mustafa A. Mahmutoglu
- Department of Neuroradiology (G.B., U.N., M.A.M., M.F., Ch.H., S.H., C.U., M.B., M.A.M., J.A.R.P., P.V.), Heidelberg University Hospital, Germany
| | - Martha Foltyn
- Department of Neuroradiology (G.B., U.N., M.A.M., M.F., Ch.H., S.H., C.U., M.B., M.A.M., J.A.R.P., P.V.), Heidelberg University Hospital, Germany
| | - Christian Herweh
- Department of Neuroradiology (G.B., U.N., M.A.M., M.F., Ch.H., S.H., C.U., M.B., M.A.M., J.A.R.P., P.V.), Heidelberg University Hospital, Germany
| | - Simon Nagel
- Neurology Clinic (S.N., S.S., P.A.R.), Heidelberg University Hospital, Germany
| | | | - Sabine Heiland
- Department of Neuroradiology (G.B., U.N., M.A.M., M.F., Ch.H., S.H., C.U., M.B., M.A.M., J.A.R.P., P.V.), Heidelberg University Hospital, Germany
| | - Christian Ulfert
- Department of Neuroradiology (G.B., U.N., M.A.M., M.F., Ch.H., S.H., C.U., M.B., M.A.M., J.A.R.P., P.V.), Heidelberg University Hospital, Germany
| | | | - Martin Bendszus
- Department of Neuroradiology (G.B., U.N., M.A.M., M.F., Ch.H., S.H., C.U., M.B., M.A.M., J.A.R.P., P.V.), Heidelberg University Hospital, Germany
| | - Markus A. Möhlenbruch
- Department of Neuroradiology (G.B., U.N., M.A.M., M.F., Ch.H., S.H., C.U., M.B., M.A.M., J.A.R.P., P.V.), Heidelberg University Hospital, Germany
| | - Johannes A.R. Pfaff
- Department of Neuroradiology (G.B., U.N., M.A.M., M.F., Ch.H., S.H., C.U., M.B., M.A.M., J.A.R.P., P.V.), Heidelberg University Hospital, Germany
| | - Philipp Vollmuth
- Department of Neuroradiology (G.B., U.N., M.A.M., M.F., Ch.H., S.H., C.U., M.B., M.A.M., J.A.R.P., P.V.), Heidelberg University Hospital, Germany
| |
Collapse
|
41
|
Abstract
Artificial intelligence (AI) is a technology that utilizes machines to mimic intelligent human behavior. To appreciate human-technology interaction in the clinical setting, augmented intelligence has been proposed as a cognitive extension of AI in health care, emphasizing its assistive and supplementary role to medical professionals. While truly autonomous medical robotic systems are still beyond reach, the virtual component of AI, known as software-type algorithms, is the main component used in dentistry. Because of their powerful capabilities in data analysis, these virtual algorithms are expected to improve the accuracy and efficacy of dental diagnosis, provide visualized anatomic guidance for treatment, simulate and evaluate prospective results, and project the occurrence and prognosis of oral diseases. Potential obstacles in contemporary algorithms that prevent routine implementation of AI include the lack of data curation, sharing, and readability; the inability to illustrate the inner decision-making process; the insufficient power of classical computing; and the neglect of ethical principles in the design of AI frameworks. It is necessary to maintain a proactive attitude toward AI to ensure its affirmative development and promote human-technology rapport to revolutionize dental practice. The present review outlines the progress and potential dental applications of AI in medical-aided diagnosis, treatment, and disease prediction and discusses their data limitations, interpretability, computing power, and ethical considerations, as well as their impact on dentists, with the objective of creating a backdrop for future research in this rapidly expanding arena.
Collapse
Affiliation(s)
- T Shan
- Department of Operative Dentistry and Endodontics, Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
| | - F R Tay
- The Dental College of Georgia, Augusta University, Augusta, GA, USA
| | - L Gu
- Department of Operative Dentistry and Endodontics, Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
| |
Collapse
|
42
|
Hüsers J, Hafer G, Heggemann J, Wiemeyer S, John SM, Hübner U. Predicting the amputation risk for patients with diabetic foot ulceration - a Bayesian decision support tool. BMC Med Inform Decis Mak 2020; 20:200. [PMID: 32838777 PMCID: PMC7446175 DOI: 10.1186/s12911-020-01195-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Accepted: 07/20/2020] [Indexed: 01/06/2023] Open
Abstract
Background Diabetes mellitus is a major global health issue with a growing prevalence. In this context, the number of diabetic complications is also on the rise, such as diabetic foot ulcers (DFU), which are closely linked to the risk of lower extremity amputation (LEA). Statistical prediction tools may support clinicians to initiate early tertiary LEA prevention for DFU patients. Thus, we designed Bayesian prediction models, as they produce transparent decision rules, quantify uncertainty intuitively and acknowledge prior available scientific knowledge. Method A logistic regression using observational collected according to the standardised PEDIS classification was utilised to compute the six-month amputation risk of DFU patients for two types of LEA: 1.) any-amputation and 2.) major-amputation. Being able to incorporate information which is available before the analysis, the Bayesian models were fitted following a twofold strategy. First, the designed prediction models waive the available information and, second, we incorporated the a priori available scientific knowledge into our models. Then, we evaluated each model with respect to the effect of the predictors and validity of the models. Next, we compared the performance of both models with respect to the incorporation of prior knowledge. Results This study included 237 patients. The mean age was 65.9 (SD 12.3), and 83.5% were male. Concerning the outcome, 31.6% underwent any- and 12.2% underwent a major-amputation procedure. The risk factors of perfusion, ulcer extent and depth revealed an impact on the outcomes, whereas the infection status and sensation did not. The major-amputation model using prior information outperformed the uninformed counterpart (AUC 0.765 vs AUC 0.790, Cohen’s d 2.21). In contrast, the models predicting any-amputation performed similarly (0.793 vs 0.790, Cohen’s d 0.22). Conclusions Both of the Bayesian amputation risk models showed acceptable prognostic values, and the major-amputation model benefitted from incorporating a priori information from a previous study. Thus, PEDIS serves as a valid foundation for a clinical decision support tool for the prediction of the amputation risk in DFU patients. Furthermore, we demonstrated the use of the available prior scientific information within a Bayesian framework to establish chains of knowledge.
Collapse
Affiliation(s)
- Jens Hüsers
- Health Informatics Research Group, Department of Business Management and Social Sciences, University of Applied Sciences Osnabrück, Osnabrück, Germany
| | - Guido Hafer
- Niels Stensen Kliniken, Christliches Klinikum, Melle, Germany
| | - Jan Heggemann
- Niels Stensen Kliniken, Christliches Klinikum, Melle, Germany
| | - Stefan Wiemeyer
- Niels Stensen Kliniken, Christliches Klinikum, Melle, Germany
| | - Swen Malte John
- Department Dermatology, Environmental Medicine, Health Theory, University of Osnabrück, Osnabruck, Germany
| | - Ursula Hübner
- Health Informatics Research Group, Department of Business Management and Social Sciences, University of Applied Sciences Osnabrück, Osnabrück, Germany.
| |
Collapse
|
43
|
Mallappallil M, Sabu J, Gruessner A, Salifu M. A review of big data and medical research. SAGE Open Med 2020; 8:2050312120934839. [PMID: 32637104 PMCID: PMC7323266 DOI: 10.1177/2050312120934839] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2019] [Accepted: 05/21/2020] [Indexed: 12/11/2022] Open
Abstract
Universally, the volume of data has increased, with the collection rate doubling every 40 months, since the 1980s. "Big data" is a term that was introduced in the 1990s to include data sets too large to be used with common software. Medicine is a major field predicted to increase the use of big data in 2025. Big data in medicine may be used by commercial, academic, government, and public sectors. It includes biologic, biometric, and electronic health data. Examples of biologic data include biobanks; biometric data may have individual wellness data from devices; electronic health data include the medical record; and other data demographics and images. Big data has also contributed to the changes in the research methodology. Changes in the clinical research paradigm has been fueled by large-scale biological data harvesting (biobanks), which is developed, analyzed, and managed by cheaper computing technology (big data), supported by greater flexibility in study design (real-world data) and the relationships between industry, government regulators, and academics. Cultural changes along with easy access to information via the Internet facilitate ease of participation by more people. Current needs demand quick answers which may be supplied by big data, biobanks, and changes in flexibility in study design. Big data can reveal health patterns, and promises to provide solutions that have previously been out of society's grasp; however, the murkiness of international laws, questions of data ownership, public ignorance, and privacy and security concerns are slowing down the progress that could otherwise be achieved by the use of big data. The goal of this descriptive review is to create awareness of the ramifications for big data and to encourage readers that this trend is positive and will likely lead to better clinical solutions, but, caution must be exercised to reduce harm.
Collapse
Affiliation(s)
| | - Jacob Sabu
- State University of New York at Downstate, Brooklyn, NY, USA
| | | | - Moro Salifu
- State University of New York at Downstate, Brooklyn, NY, USA
| |
Collapse
|
44
|
Faviez C, Chen X, Garcelon N, Neuraz A, Knebelmann B, Salomon R, Lyonnet S, Saunier S, Burgun A. Diagnosis support systems for rare diseases: a scoping review. Orphanet J Rare Dis 2020; 15:94. [PMID: 32299466 PMCID: PMC7164220 DOI: 10.1186/s13023-020-01374-z] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Accepted: 03/31/2020] [Indexed: 12/14/2022] Open
Abstract
INTRODUCTION Rare diseases affect approximately 350 million people worldwide. Delayed diagnosis is frequent due to lack of knowledge of most clinicians and a small number of expert centers. Consequently, computerized diagnosis support systems have been developed to address these issues, with many relying on rare disease expertise and taking advantage of the increasing volume of generated and accessible health-related data. Our objective is to perform a review of all initiatives aiming to support the diagnosis of rare diseases. METHODS A scoping review was conducted based on methods proposed by Arksey and O'Malley. A charting form for relevant study analysis was developed and used to categorize data. RESULTS Sixty-eight studies were retained at the end of the charting process. Diagnosis targets varied from 1 rare disease to all rare diseases. Material used for diagnosis support consisted mostly of phenotype concepts, images or fluids. Fifty-seven percent of the studies used expert knowledge. Two-thirds of the studies relied on machine learning algorithms, and one-third used simple similarities. Manual algorithms were encountered as well. Most of the studies presented satisfying performance of evaluation by comparison with references or with external validation. Fourteen studies provided online tools, most of which aimed to support the diagnosis of all rare diseases by considering queries based on phenotype concepts. CONCLUSION Numerous solutions relying on different materials and use of various methodologies are emerging with satisfying preliminary results. However, the variability of approaches and evaluation processes complicates the comparison of results. Efforts should be made to adequately validate these tools and guarantee reproducibility and explicability.
Collapse
Affiliation(s)
- Carole Faviez
- Centre de Recherche des Cordeliers, INSERM, Université de Paris, Sorbonne Université, F-75006, Paris, France.
| | - Xiaoyi Chen
- Centre de Recherche des Cordeliers, INSERM, Université de Paris, Sorbonne Université, F-75006, Paris, France
| | - Nicolas Garcelon
- Centre de Recherche des Cordeliers, INSERM, Université de Paris, Sorbonne Université, F-75006, Paris, France.,Institut Imagine, Université de Paris, F-75015, Paris, France
| | - Antoine Neuraz
- Centre de Recherche des Cordeliers, INSERM, Université de Paris, Sorbonne Université, F-75006, Paris, France.,Département d'informatique médicale, Hôpital Necker-Enfants Malades, Assistance Publique - Hôpitaux de Paris (AP-HP), F-75015, Paris, France
| | - Bertrand Knebelmann
- Service de Néphrologie Transplantation Adultes, Hôpital Necker-Enfants Malades, F-75015, Paris, France.,Université de Paris, F-75006, Paris, France.,Institut Necker-Enfants Malades, INSERM, Hôpital Necker-Enfants Malades, F-75015, Paris, France
| | - Rémi Salomon
- Institut Imagine, Université de Paris, F-75015, Paris, France.,Service de Néphrologie Pédiatrique, Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris (AP-HP), Université de Paris, F-75015, Paris, France
| | - Stanislas Lyonnet
- Université de Paris, F-75006, Paris, France.,Laboratory of Embryology and Genetics of Congenital Malformations, INSERM UMR 1163, Université de Paris, Imagine Institute, F-75015, Paris, France.,Service de génétique, Hôpital Necker-Enfants Malades, Assistance Publique - Hôpitaux de Paris (AP-HP), F-75015, Paris, France
| | - Sophie Saunier
- Université de Paris, F-75006, Paris, France.,Laboratory of Renal Hereditary Diseases, INSERM UMR 1163, Université de Paris, Imagine Institute, F-75015, Paris, France
| | - Anita Burgun
- Centre de Recherche des Cordeliers, INSERM, Université de Paris, Sorbonne Université, F-75006, Paris, France.,Département d'informatique médicale, Hôpital Necker-Enfants Malades, Assistance Publique - Hôpitaux de Paris (AP-HP), F-75015, Paris, France.,Université de Paris, F-75006, Paris, France.,PaRis Artificial Intelligence Research InstitutE (PRAIRIE), Paris, France
| |
Collapse
|
45
|
Koutkias V, Bouaud J. Contributions on Clinical Decision Support from the 2018 Literature. Yearb Med Inform 2019; 28:135-137. [PMID: 31419825 PMCID: PMC6697519 DOI: 10.1055/s-0039-1677929] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Objectives
: To summarize recent research and select the best papers published in 2018 in the field of computerized clinical decision support for the Decision Support section of the International Medical Informatics Association (IMIA) yearbook.
Methods
: A literature review was performed by searching two bibliographic databases for papers referring to clinical decision support systems (CDSSs). The aim was to identify a list of candidate best papers from the retrieved bibliographic records, which were then peer-reviewed by external reviewers. A consensus meeting of the IMIA editorial team finally selected the best papers on the basis of all reviews and the section editors' evaluation.
Results
: Among 1,148 retrieved articles, 15 best paper candidates were selected, the review of which resulted in the selection of four best papers. The first paper introduces a deep learning model for estimating short-term life expectancy (>3 months) of metastatic cancer patients by analyzing free-text clinical notes in electronic medical records, while maintaining the temporal visit sequence. The second paper takes note that CDSSs become routinely integrated in health information systems and compares statistical anomaly detection models to identify CDSS malfunctions which, if remain unnoticed, may have a negative impact on care delivery. The third paper fairly reports on lessons learnt from the development of an oncology CDSS using artificial intelligence techniques and from its assessment in a large US cancer center. The fourth paper implements a preference learning methodology for detecting inconsistencies in clinical practice guidelines and illustrates the applicability of the proposed methodology to antibiotherapy.
Conclusions
: Three of the four best papers rely on data-driven methods, and one builds on a knowledge-based approach. While there is currently a trend for data-driven decision support, the promising results of such approaches still need to be confirmed by the adoption of these systems and their routine use.
Collapse
Affiliation(s)
- Vassilis Koutkias
- Institute of Applied Biosciences, Centre for Research & Technology Hellas, Thermi, Thessaloniki, Greece
| | - Jacques Bouaud
- AP-HP, Delegation for Clinical Research and Innovation, Paris, France.,Sorbonne Université, Université Paris 13, Sorbonne Paris Cité, INSERM, UMR_S 1142, LIMICS, Paris, France
| | | |
Collapse
|