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Tran TK, Tran MC, Joseph A, Phan PA, Grau V, Farmery AD. A systematic review of machine learning models for management, prediction and classification of ARDS. Respir Res 2024; 25:232. [PMID: 38834976 DOI: 10.1186/s12931-024-02834-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 05/04/2024] [Indexed: 06/06/2024] Open
Abstract
AIM Acute respiratory distress syndrome or ARDS is an acute, severe form of respiratory failure characterised by poor oxygenation and bilateral pulmonary infiltrates. Advancements in signal processing and machine learning have led to promising solutions for classification, event detection and predictive models in the management of ARDS. METHOD In this review, we provide systematic description of different studies in the application of Machine Learning (ML) and artificial intelligence for management, prediction, and classification of ARDS. We searched the following databases: Google Scholar, PubMed, and EBSCO from 2009 to 2023. A total of 243 studies was screened, in which, 52 studies were included for review and analysis. We integrated knowledge of previous work providing the state of art and overview of explainable decision models in machine learning and have identified areas for future research. RESULTS Gradient boosting is the most common and successful method utilised in 12 (23.1%) of the studies. Due to limitation of data size available, neural network and its variation is used by only 8 (15.4%) studies. Whilst all studies used cross validating technique or separated database for validation, only 1 study validated the model with clinician input. Explainability methods were presented in 15 (28.8%) of studies with the most common method is feature importance which used 14 times. CONCLUSION For databases of 5000 or fewer samples, extreme gradient boosting has the highest probability of success. A large, multi-region, multi centre database is required to reduce bias and take advantage of neural network method. A framework for validating with and explaining ML model to clinicians involved in the management of ARDS would be very helpful for development and deployment of the ML model.
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Affiliation(s)
- Tu K Tran
- Department of Engineering and Science, University of Oxford, Oxford, UK.
- Nuffield Department of Clinical Neurosciences, Oxford Institute of Biomedical Engineering, University of Oxford, Oxford, UK.
| | - Minh C Tran
- Nuffield Division of Anaesthetics, University of Oxford, Oxford, UK
| | - Arun Joseph
- Nuffield Division of Anaesthetics, University of Oxford, Oxford, UK
| | - Phi A Phan
- Nuffield Division of Anaesthetics, University of Oxford, Oxford, UK
| | - Vicente Grau
- Department of Engineering and Science, University of Oxford, Oxford, UK
| | - Andrew D Farmery
- Nuffield Division of Anaesthetics, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, Oxford Institute of Biomedical Engineering, University of Oxford, Oxford, UK
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Vitt JR, Mainali S. Artificial Intelligence and Machine Learning Applications in Critically Ill Brain Injured Patients. Semin Neurol 2024; 44:342-356. [PMID: 38569520 DOI: 10.1055/s-0044-1785504] [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: 04/05/2024]
Abstract
The utilization of Artificial Intelligence (AI) and Machine Learning (ML) is paving the way for significant strides in patient diagnosis, treatment, and prognostication in neurocritical care. These technologies offer the potential to unravel complex patterns within vast datasets ranging from vast clinical data and EEG (electroencephalogram) readings to advanced cerebral imaging facilitating a more nuanced understanding of patient conditions. Despite their promise, the implementation of AI and ML faces substantial hurdles. Historical biases within training data, the challenge of interpreting multifaceted data streams, and the "black box" nature of ML algorithms present barriers to widespread clinical adoption. Moreover, ethical considerations around data privacy and the need for transparent, explainable models remain paramount to ensure trust and efficacy in clinical decision-making.This article reflects on the emergence of AI and ML as integral tools in neurocritical care, discussing their roles from the perspective of both their scientific promise and the associated challenges. We underscore the importance of extensive validation in diverse clinical settings to ensure the generalizability of ML models, particularly considering their potential to inform critical medical decisions such as withdrawal of life-sustaining therapies. Advancement in computational capabilities is essential for implementing ML in clinical settings, allowing for real-time analysis and decision support at the point of care. As AI and ML are poised to become commonplace in clinical practice, it is incumbent upon health care professionals to understand and oversee these technologies, ensuring they adhere to the highest safety standards and contribute to the realization of personalized medicine. This engagement will be pivotal in integrating AI and ML into patient care, optimizing outcomes in neurocritical care through informed and data-driven decision-making.
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Affiliation(s)
- Jeffrey R Vitt
- Department of Neurological Surgery, UC Davis Medical Center, Sacramento, California
| | - Shraddha Mainali
- Department of Neurology, Virginia Commonwealth University, Richmond, Virginia
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Duffy SS, Lee S, Gottlieb Sen D. Pediatric Monitoring Technologies and Congenital Heart Disease: A Systematic Review. World J Pediatr Congenit Heart Surg 2024:21501351241247500. [PMID: 38807505 DOI: 10.1177/21501351241247500] [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: 05/30/2024]
Abstract
Outpatient monitoring of infants with congenital heart disease has been shown to significantly reduce rates of mortality in the single ventricle population. Despite the accelerating development of miniaturized biosensors and electronics, and a growing market demand for at-home monitoring devices, the application of these technologies to infants and children is significantly delayed compared with the development of devices for adults. This article aims to review the current landscape of available monitoring technologies and devices for pediatric patients to describe the gap between technologies and clinical needs with the goal of progressing development of clinically and scientifically validated pediatric monitoring devices.
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Affiliation(s)
- Summer S Duffy
- School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Sharon Lee
- Krieger School of Arts and Sciences, Johns Hopkins University, Baltimore, MD, USA
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Armoundas AA, Narayan SM, Arnett DK, Spector-Bagdady K, Bennett DA, Celi LA, Friedman PA, Gollob MH, Hall JL, Kwitek AE, Lett E, Menon BK, Sheehan KA, Al-Zaiti SS. Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association. Circulation 2024; 149:e1028-e1050. [PMID: 38415358 PMCID: PMC11042786 DOI: 10.1161/cir.0000000000001201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/29/2024]
Abstract
A major focus of academia, industry, and global governmental agencies is to develop and apply artificial intelligence and other advanced analytical tools to transform health care delivery. The American Heart Association supports the creation of tools and services that would further the science and practice of precision medicine by enabling more precise approaches to cardiovascular and stroke research, prevention, and care of individuals and populations. Nevertheless, several challenges exist, and few artificial intelligence tools have been shown to improve cardiovascular and stroke care sufficiently to be widely adopted. This scientific statement outlines the current state of the art on the use of artificial intelligence algorithms and data science in the diagnosis, classification, and treatment of cardiovascular disease. It also sets out to advance this mission, focusing on how digital tools and, in particular, artificial intelligence may provide clinical and mechanistic insights, address bias in clinical studies, and facilitate education and implementation science to improve cardiovascular and stroke outcomes. Last, a key objective of this scientific statement is to further the field by identifying best practices, gaps, and challenges for interested stakeholders.
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Ghanta SN, Gautam N, Mehta JL, Al’Aref SJ. Machine Learning for Predicting Intubations in Heart Failure Patients: the Challenge of the Right Approach. Cardiovasc Drugs Ther 2024; 38:211-214. [PMID: 36593325 PMCID: PMC9807425 DOI: 10.1007/s10557-022-07423-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/28/2022] [Indexed: 01/04/2023]
Affiliation(s)
- Sai Nikhila Ghanta
- Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, AR USA
| | - Nitesh Gautam
- Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, AR USA
| | - Jawahar L. Mehta
- Department of Medicine, Division of Cardiology, University of Arkansas for Medical Sciences, 4301 W. Markham St, Little Rock, AR USA
| | - Subhi J. Al’Aref
- Department of Medicine, Division of Cardiology, University of Arkansas for Medical Sciences, 4301 W. Markham St, Little Rock, AR USA
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Göndöcs D, Dörfler V. AI in medical diagnosis: AI prediction & human judgment. Artif Intell Med 2024; 149:102769. [PMID: 38462271 DOI: 10.1016/j.artmed.2024.102769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 12/02/2023] [Accepted: 01/14/2024] [Indexed: 03/12/2024]
Abstract
AI has long been regarded as a panacea for decision-making and many other aspects of knowledge work; as something that will help humans get rid of their shortcomings. We believe that AI can be a useful asset to support decision-makers, but not that it should replace decision-makers. Decision-making uses algorithmic analysis, but it is not solely algorithmic analysis; it also involves other factors, many of which are very human, such as creativity, intuition, emotions, feelings, and value judgments. We have conducted semi-structured open-ended research interviews with 17 dermatologists to understand what they expect from an AI application to deliver to medical diagnosis. We have found four aggregate dimensions along which the thinking of dermatologists can be described: the ways in which our participants chose to interact with AI, responsibility, 'explainability', and the new way of thinking (mindset) needed for working with AI. We believe that our findings will help physicians who might consider using AI in their diagnosis to understand how to use AI beneficially. It will also be useful for AI vendors in improving their understanding of how medics want to use AI in diagnosis. Further research will be needed to examine if our findings have relevance in the wider medical field and beyond.
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Affiliation(s)
| | - Viktor Dörfler
- University of Strathclyde Business School, United Kingdom.
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O'Reilly D, McGrath J, Martin-Loeches I. Optimizing artificial intelligence in sepsis management: Opportunities in the present and looking closely to the future. JOURNAL OF INTENSIVE MEDICINE 2024; 4:34-45. [PMID: 38263963 PMCID: PMC10800769 DOI: 10.1016/j.jointm.2023.10.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Revised: 09/29/2023] [Accepted: 10/01/2023] [Indexed: 01/25/2024]
Abstract
Sepsis remains a major challenge internationally for healthcare systems. Its incidence is rising due to poor public awareness and delays in its recognition and subsequent management. In sepsis, mortality increases with every hour left untreated. Artificial intelligence (AI) is transforming worldwide healthcare delivery at present. This review has outlined how AI can augment strategies to address this global disease burden. AI and machine learning (ML) algorithms can analyze vast quantities of increasingly complex clinical datasets from electronic medical records to assist clinicians in diagnosing and treating sepsis earlier than traditional methods. Our review highlights how these models can predict the risk of sepsis and organ failure even before it occurs. This gives providers additional time to plan and execute treatment plans, thereby avoiding increasing complications associated with delayed diagnosis of sepsis. The potential for cost savings with AI implementation is also discussed, including improving workflow efficiencies, reducing administrative costs, and improving healthcare outcomes. Despite these advantages, clinicians have been slow to adopt AI into clinical practice. Some of the limitations posed by AI solutions include the lack of diverse data sets for model building so that they are widely applicable for routine clinical use. Furthermore, the subsequent algorithms are often based on complex mathematics leading to clinician hesitancy to embrace such technologies. Finally, we highlight the need for robust political and regulatory frameworks in this area to achieve the trust and approval of clinicians and patients to implement this transformational technology.
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Affiliation(s)
- Darragh O'Reilly
- Department of Intensive Care Medicine, Multidisciplinary Intensive Care Research Organization (MICRO), St James’ Hospital, Dublin, Ireland
| | - Jennifer McGrath
- Department of Intensive Care Medicine, Multidisciplinary Intensive Care Research Organization (MICRO), St James’ Hospital, Dublin, Ireland
| | - Ignacio Martin-Loeches
- Department of Intensive Care Medicine, Multidisciplinary Intensive Care Research Organization (MICRO), St James’ Hospital, Dublin, Ireland
- Department of Respiratory Intensive care, Hospital Clinic, Universitat de Barcelona, IDIBAPS, CIBERES, Barcelona, Spain
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Gentilin A. Challenges in normalizing pulse wave velocity scores: Implications for assessing central artery stiffness. Vascular 2023:17085381231194145. [PMID: 37553123 DOI: 10.1177/17085381231194145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/10/2023]
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Gálvez-Barrón C, Pérez-López C, Villar-Álvarez F, Ribas J, Formiga F, Chivite D, Boixeda R, Iborra C, Rodríguez-Molinero A. Machine learning for the development of diagnostic models of decompensated heart failure or exacerbation of chronic obstructive pulmonary disease. Sci Rep 2023; 13:12709. [PMID: 37543661 PMCID: PMC10404284 DOI: 10.1038/s41598-023-39329-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 07/24/2023] [Indexed: 08/07/2023] Open
Abstract
Heart failure (HF) and chronic obstructive pulmonary disease (COPD) are two chronic diseases with the greatest adverse impact on the general population, and early detection of their decompensation is an important objective. However, very few diagnostic models have achieved adequate diagnostic performance. The aim of this trial was to develop diagnostic models of decompensated heart failure or COPD exacerbation with machine learning techniques based on physiological parameters. A total of 135 patients hospitalized for decompensated heart failure and/or COPD exacerbation were recruited. Each patient underwent three evaluations: one in the decompensated phase (during hospital admission) and two more consecutively in the compensated phase (at home, 30 days after discharge). In each evaluation, heart rate (HR) and oxygen saturation (Ox) were recorded continuously (with a pulse oximeter) during a period of walking for 6 min, followed by a recovery period of 4 min. To develop the diagnostic models, predictive characteristics related to HR and Ox were initially selected through classification algorithms. Potential predictors included age, sex and baseline disease (heart failure or COPD). Next, diagnostic classification models (compensated vs. decompensated phase) were developed through different machine learning techniques. The diagnostic performance of the developed models was evaluated according to sensitivity (S), specificity (E) and accuracy (A). Data from 22 patients with decompensated heart failure, 25 with COPD exacerbation and 13 with both decompensated pathologies were included in the analyses. Of the 96 characteristics of HR and Ox initially evaluated, 19 were selected. Age, sex and baseline disease did not provide greater discriminative power to the models. The techniques with S and E values above 80% were the logistic regression (S: 80.83%; E: 86.25%; A: 83.61%) and support vector machine (S: 81.67%; E: 85%; A: 82.78%) techniques. The diagnostic models developed achieved good diagnostic performance for decompensated HF or COPD exacerbation. To our knowledge, this study is the first to report diagnostic models of decompensation potentially applicable to both COPD and HF patients. However, these results are preliminary and warrant further investigation to be confirmed.
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Affiliation(s)
- César Gálvez-Barrón
- Research Area, Consorci Sanitari Alt Penedès i Garraf, Sant Pere de Ribes-Barcelona, Barcelona, Spain.
| | - Carlos Pérez-López
- Research Area, Consorci Sanitari Alt Penedès i Garraf, Sant Pere de Ribes-Barcelona, Barcelona, Spain
| | | | - Jesús Ribas
- Department of Pneumology, Hospital Universitari de Bellvitge, Barcelona, Spain
| | - Francesc Formiga
- Geriatric Unit, Department of Internal Medicine, Hospital Universitari de Bellvitge, Barcelona, Spain
| | - David Chivite
- Geriatric Unit, Department of Internal Medicine, Hospital Universitari de Bellvitge, Barcelona, Spain
| | - Ramón Boixeda
- Department of Internal Medicine, Hospital de Mataró, Mataró-Barcelona, Spain
| | - Cristian Iborra
- Department of Cardiology, IIS Fundación Jiménez Díaz, Madrid, Spain
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Mehrpour O, Nakhaee S, Saeedi F, Valizade B, Lotfi E, Nawaz MH. Utility of artificial intelligence to identify antihyperglycemic agents poisoning in the USA: introducing a practical web application using National Poison Data System (NPDS). ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:57801-57810. [PMID: 36973614 DOI: 10.1007/s11356-023-26605-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 03/18/2023] [Indexed: 05/10/2023]
Abstract
Clinical effects of antihyperglycemic agents poisoning may overlap each other. So, distinguishing exposure to these pharmaceutical drugs may take work. This study examined the application of machine learning techniques in identifying antihyperglycemic agent exposure using the national poisoning database in the USA. In this study, the data of single exposure due to Biguanides and Sulfonylureas (n=6183) was requested from the National Poison Data System (NPDS) for 2014-2018. We have tried five machine learning models (random forest classifier, k-nearest neighbors, Xgboost classifier, logistic regression, neural network Keras). For the multiclass classification modeling, we have divided the dataset into two parts: train (75%) and test (25%). The performance metrics used were accuracy, specificity, precision, recall, and F1-score. The algorithms used to get the classification results of different models to diagnose antihyperglycemic agents were very accurate. The accuracy of our model in determining these two antihyperglycemic agents was 91-93%. The precision-recall curve showed average precision of 0.91, 0.97, 0.97, and 0.98 for k-nearest neighbors, logistic regression, random forest, and XGB, respectively. The logistic regression, random forest, and XGB had the highest AUC (AUC=0.97) among both biguanides and sulfonylureas groups. The negative predictive values (NPV) for all the models were between 89 and 93%. We introduced a practical web application to help physicians distinguish between these agents. Despite variations in accuracy among the different types of algorithms used, all of them could accurately determine the specific exposure to biguanides and sulfonylureas retrospectively. Machine learning can distinguish antihyperglycemic agents, which may be useful for physicians without any background in medical toxicology. Besides, Our suggested ML-based Web application might help physicians in their diagnosis.
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Affiliation(s)
- Omid Mehrpour
- AI and Health LLC, Tucson, AZ, USA.
- Rocky Mountain Poison & Drug Safety, Denver Health, and Hospital Authority, Denver, CO, USA.
| | - Samaneh Nakhaee
- Medical Toxicology and Drug Abuse Research Center (MTDRC), Birjand University of Medical Sciences (BUMS), Birjand, Iran
| | - Farhad Saeedi
- Medical Toxicology and Drug Abuse Research Center (MTDRC), Birjand University of Medical Sciences (BUMS), Birjand, Iran
- Student Research Committee, Birjand University of Medical Sciences, Birjand, Iran
| | - Bahare Valizade
- Medical Toxicology and Drug Abuse Research Center (MTDRC), Birjand University of Medical Sciences (BUMS), Birjand, Iran
| | - Erfan Lotfi
- Medical Toxicology and Drug Abuse Research Center (MTDRC), Birjand University of Medical Sciences (BUMS), Birjand, Iran
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Mehrpour O, Hoyte C, Al Masud A, Biswas A, Schimmel J, Nakhaee S, Nasr MS, Delva-Clark H, Goss F. Deep learning neural network derivation and testing to distinguish acute poisonings. Expert Opin Drug Metab Toxicol 2023; 19:367-380. [PMID: 37395108 DOI: 10.1080/17425255.2023.2232724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Accepted: 06/30/2023] [Indexed: 07/04/2023]
Abstract
INTRODUCTION Acute poisoning is a significant global health burden, and the causative agent is often unclear. The primary aim of this pilot study was to develop a deep learning algorithm that predicts the most probable agent a poisoned patient was exposed to from a pre-specified list of drugs. RESEARCH DESIGN & METHODS Data were queried from the National Poison Data System (NPDS) from 2014 through 2018 for eight single-agent poisonings (acetaminophen, diphenhydramine, aspirin, calcium channel blockers, sulfonylureas, benzodiazepines, bupropion, and lithium). Two Deep Neural Networks (PyTorch and Keras) designed for multi-class classification tasks were applied. RESULTS There were 201,031 single-agent poisonings included in the analysis. For distinguishing among selected poisonings, PyTorch model had specificity of 97%, accuracy of 83%, precision of 83%, recall of 83%, and a F1-score of 82%. Keras had specificity of 98%, accuracy of 83%, precision of 84%, recall of 83%, and a F1-score of 83%. The best performance was achieved in the diagnosis of single-agent poisoning in diagnosing poisoning by lithium, sulfonylureas, diphenhydramine, calcium channel blockers, then acetaminophen, in PyTorch (F1-score = 99%, 94%, 85%, 83%, and 82%, respectively) and Keras (F1-score = 99%, 94%, 86%, 82%, and 82%, respectively). CONCLUSION Deep neural networks can potentially help in distinguishing the causative agent of acute poisoning. This study used a small list of drugs, with polysubstance ingestions excluded.Reproducible source code and results can be obtained at https://github.com/ashiskb/npds-workspace.git.
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Affiliation(s)
- Omid Mehrpour
- Michigan Poison & Drug Information Center, Wayne State University School of Medicine, Detroit, MI, USA
| | - Christopher Hoyte
- Department of Emergency Medicine, University of Colorado School of Medicine, Aurora, CO, USA
| | | | - Ashis Biswas
- Department of Computer Science and Engineering, University of Colorado, Denver, CO, USA
| | - Jonathan Schimmel
- Department of Emergency Medicine, Division of Medical Toxicology, Mount Sinai Hospital Icahn School of Medicine, New York, NY, USA
| | - Samaneh Nakhaee
- Medical Toxicology and Drug Abuse Research Center (MTDRC), Birjand University of Medical Sciences (BUMS), Birjand, Iran
| | - Mohammad Sadegh Nasr
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX, USA
| | | | - Foster Goss
- Department of Emergency Medicine, University of Colorado School of Medicine, Aurora, CO, USA
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Scherer L, Kuss M, Nahm W. Review of Artificial Intelligence-Based Signal Processing in Dialysis: Challenges for Machine-Embedded and Complementary Applications. ADVANCES IN KIDNEY DISEASE AND HEALTH 2023; 30:40-46. [PMID: 36723281 DOI: 10.1053/j.akdh.2022.11.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 08/23/2022] [Accepted: 11/07/2022] [Indexed: 01/20/2023]
Abstract
Artificial intelligence technology is trending in nearly every medical area. It offers the possibility for improving analytics, therapy outcome, and user experience during therapy. In dialysis, the application of artificial intelligence as a therapy-individualization tool is led more by start-ups than consolidated players, and innovation in dialysis seems comparably stagnant. Factors such as technical requirements or regulatory processes are important and necessary but can slow down the implementation of artificial intelligence due to missing data infrastructure and undefined approval processes. Current research focuses mainly on analyzing health records or wearable technology to add to existing health data. It barely uses signal data from treatment devices to apply artificial intelligence models. This article, therefore, discusses requirements for signal processing through artificial intelligence in health care and compares these with the status quo in dialysis therapy. It offers solutions for given barriers to speed up innovation with sensor data, opening access to existing and untapped sources, and shows the unique advantage of signal processing in dialysis compared to other health care domains. This research shows that even though the combination of different data is vital for improving patients' therapy, adding signal-based treatment data from dialysis devices to the picture can benefit the understanding of treatment dynamics, improving and individualizing therapy.
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Affiliation(s)
- Lena Scherer
- Karlsruhe Institute of Technology, Karlsruhe, Germany.
| | | | - Werner Nahm
- Karlsruhe Institute of Technology, Karlsruhe, Germany
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Mol CG, Vieira AGDS, Garcia BMSP, Pereira EDS, Eid RAC, Pinto ACPN, Nawa RK. Closed-loop oxygen control for patients with hypoxaemia during hospitalisation: a living systematic review and meta-analysis protocol. BMJ Open 2022; 12:e062299. [PMID: 36523244 PMCID: PMC9748949 DOI: 10.1136/bmjopen-2022-062299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 11/10/2022] [Indexed: 12/14/2022] Open
Abstract
INTRODUCTION Oxygen is the most common drug used in critical care patients to correct episodes of hypoxaemia. The adoption of new technologies in clinical practice, such as closed-loop systems for an automatic oxygen titration, may improve outcomes and reduce the healthcare professionals' workload at the bedside; however, certainty of the evidence regarding the safety and benefits still remains low. We aim to evaluate the effectiveness, efficacy and safety of the closed-loop oxygen control for patients with hypoxaemia during the hospitalisation period by conducting a systematic review and meta-analysis. METHODS AND ANALYSIS MEDLINE, CENTRAL, EMBASE, LILACS, CINAHL and LOVE evidence databases will be searched. Randomised controlled trials and cross-over studies investigating the PICO (Population, Intervention, Comparator and Outcome) framework will be included. The primary outcomes will be the time in the peripheral oxygen saturation target. Secondary outcomes will include time for oxygen weaning time; length of stay; costs; adverse events; mortality; healthcare professionals' workload, and percentage of time with hypoxia and hyperoxia. Two reviewers will independently screen and extract data and perform quality assessment of included studies. The Cochrane risk of bias tool will be used to assess risk of bias. The RevMan V.5.4 software will be used for statistical analysis. Heterogeneity will be analysed using I2 statistics. Mean difference or standardised mean difference with 95% CI and p value will be used to calculate treatment effect for outcome variables. ETHICS AND DISSEMINATION Ethical approval is not required because this systematic review and meta-analysis is based on previously published data. Final results will be published in peer-reviewed journals and presented at relevant conferences and events. PROSPERO REGISTRATION NUMBER CRD42022306033.
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Affiliation(s)
| | | | | | | | | | - Ana Carolina Pereira Nunes Pinto
- Biological and Health Sciences Department, Universidade Federal do Amapá, Macapá, AP, Brazil
- Evidence-Based Health Program, Universidade Federal de São Paulo - UNIFESP, São Paulo, SP, Brazil
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Silva CAO, Morillo CA, Leite-Castro C, González-Otero R, Bessani M, González R, Castellanos JC, Otero L. Machine learning for atrial fibrillation risk prediction in patients with sleep apnea and coronary artery disease. Front Cardiovasc Med 2022; 9:1050409. [PMID: 36568544 PMCID: PMC9768180 DOI: 10.3389/fcvm.2022.1050409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 11/22/2022] [Indexed: 12/12/2022] Open
Abstract
Background Patients with sleep apnea (SA) and coronary artery disease (CAD) are at higher risk of atrial fibrillation (AF) than the general population. Our objectives were: to evaluate the role of CAD and SA in determining AF risk through cluster and survival analysis, and to develop a risk model for predicting AF. Methods Electronic medical record (EMR) database from 22,302 individuals including 10,202 individuals with AF, CAD, and SA, and 12,100 individuals without these diseases were analyzed using K-means clustering technique; k-nearest neighbor (kNN) algorithm and survival analysis. Age, sex, and diseases developed for each individual during 9 years were used for cluster and survival analysis. Results The risk models for AF, CAD, and SA were identified with high accuracy and sensitivity (0.98). Cluster analysis showed that CAD and high blood pressure (HBP) are the most prevalent diseases in the AF group, HBP is the most prevalent disease in CAD; and HBP and CAD are the most prevalent diseases in the SA group. Survival analysis demonstrated that individuals with HBP, CAD, and SA had a 1.5-fold increased risk of developing AF [hazard ratio (HR): 1.49, 95% CI: 1.18-1.87, p = 0.0041; HR: 1.46, 95% CI: 1.09-1.96, p = 0.01; HR: 1.54, 95% CI: 1.22-1.94, p = 0.0039, respectively] and individuals with chronic kidney disease (CKD) developed AF approximately 50% earlier than patients without these comorbidities in a period of 7 years (HR: 3.36, 95% CI: 1.46-7.73, p = 0.0023). Comorbidities that contributed to develop AF earlier in females compared to males in the group of 50-64 years were HBP (HR: 3.75 95% CI: 1.08-13, p = 0.04) CAD and SA in the group of 60-75 years were (HR: 2.4 95% CI: 1.18-4.86, p = 0.02; HR: 2.51, 95% CI: 1.14-5.52, p = 0.02, respectively). Conclusion Machine learning based algorithms demonstrated that CAD, SA, HBP, and CKD are significant risk factors for developing AF in a Latin-American population.
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Affiliation(s)
- Carlos A. O. Silva
- Programa de Pós-graduação em Inovação Tecnológica, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Carlos A. Morillo
- Department of Cardiac Sciences, Cumming School of Medicine, Libin Cardiovascular Institute, University of Calgary, Calgary, AB, Canada
| | - Cristiano Leite-Castro
- Departamento de Engenharia Elétrica, Escola de Engenharia, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Rafael González-Otero
- Departamento de Economía, Facultad de Ciencias Económicas y Administrativas, Pontificia Universidad Javeriana, Bogotá, Colombia
| | - Michel Bessani
- Departamento de Engenharia Elétrica, Escola de Engenharia, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | | | - Julio C. Castellanos
- Departamento de Dirección General, Hospital Universitario San Ignacio, Bogotá, Colombia
| | - Liliana Otero
- Centro de Investigaciones Odontológicas, Facultad de Odontología, Pontificia Universidad Javeriana, Bogotá, Colombia,*Correspondence: Liliana Otero,
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15
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Ng ZQP, Ling LYJ, Chew HSJ, Lau Y. The role of artificial intelligence in enhancing clinical nursing care: A scoping review. J Nurs Manag 2022; 30:3654-3674. [PMID: 34272911 DOI: 10.1111/jonm.13425] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 06/17/2021] [Accepted: 07/15/2021] [Indexed: 12/30/2022]
Abstract
AIM To present an overview of how artificial intelligence has been used to improve clinical nursing care. BACKGROUND Artificial intelligence has been reshaping the healthcare industry but little is known about its applicability in enhancing nursing care. EVALUATION A scoping review was conducted. Seven electronic databases (CINAHL, Cochrane Library, EMBASE, IEEE Xplore, PubMed, Scopus, and Web of Science) were searched from 1 January 2010 till 20 December 2020. Grey literature and reference lists of included articles were also searched. KEY ISSUES Thirty-seven studies encapsulating the use of artificial intelligence in improving clinical nursing care were included in this review. Six use cases were identified - documentation, formulating nursing diagnoses, formulating nursing care plans, patient monitoring, patient care prediction such as falls prediction (most common) and wound management. Various techniques of machine learning and classification were used for predictive analyses and to improve nurses' preparedness and management of patients' conditions CONCLUSION: This review highlighted the potential of artificial intelligence in improving the quality of nursing care. However, more randomized controlled trials in real-life healthcare settings should be conducted to enhance the rigor of evidence. IMPLICATIONS FOR NURSING MANAGEMENT Education in the application of artificial intelligence should be promoted to empower nurses to lead technological transformations and not passively trail behind others.
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Affiliation(s)
- Zi Qi Pamela Ng
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Li Ying Janice Ling
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Han Shi Jocelyn Chew
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Ying Lau
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
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16
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Nazir T, Mushhood Ur Rehman M, Asghar MR, Kalia JS. Artificial intelligence assisted acute patient journey. Front Artif Intell 2022; 5:962165. [PMID: 36267660 PMCID: PMC9577284 DOI: 10.3389/frai.2022.962165] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 09/12/2022] [Indexed: 11/13/2022] Open
Abstract
Artificial intelligence is taking the world by storm and soon will be aiding patients in their journey at the hospital. The trials and tribulations of the healthcare system during the COVID-19 pandemic have set the stage for shifting healthcare from a physical to a cyber-physical space. A physician can now remotely monitor a patient, admitting them only if they meet certain thresholds, thereby reducing the total number of admissions at the hospital. Coordination, communication, and resource management have been core issues for any industry. However, it is most accurate in healthcare. Both systems and providers are exhausted under the burden of increasing data and complexity of care delivery, increasing costs, and financial burden. Simultaneously, there is a digital transformation of healthcare in the making. This transformation provides an opportunity to create systems of care that are artificial intelligence-enabled. Healthcare resources can be utilized more justly. The wastage of financial and intellectual resources in an overcrowded healthcare system can be avoided by implementing IoT, telehealth, and AI/ML-based algorithms. It is imperative to consider the design principles of the patient's journey while simultaneously prioritizing a better user experience to alleviate physician concerns. This paper discusses the entire blueprint of the AI/ML-assisted patient journey and its impact on healthcare provision.
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Affiliation(s)
- Talha Nazir
- Research Fellow, NeuroCare.AI Neuroscience Academy, Dallas, TX, United States,*Correspondence: Talha Nazir
| | | | | | - Junaid S. Kalia
- NeuroCare.AI, Dallas, TX, United States,Neurologypocketbook.com, Dallas, TX, United States,Junaid S. Kalia
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17
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Singh P, Nagori A, Lodha R, Sethi T. Early prediction of hypothermia in pediatric intensive care units using machine learning. Front Physiol 2022; 13:921884. [PMID: 36171970 PMCID: PMC9511412 DOI: 10.3389/fphys.2022.921884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Accepted: 07/28/2022] [Indexed: 11/13/2022] Open
Abstract
Hypothermia is a life-threatening condition where the temperature of the body drops below 35°C and is a key source of concern in Intensive Care Units (ICUs). Early identification can help to nudge clinical management to initiate early interventions. Despite its importance, very few studies have focused on the early prediction of hypothermia. In this study, we aim to monitor and predict Hypothermia (30 min-4 h) ahead of its onset using machine learning (ML) models developed on physiological vitals and to prospectively validate the best performing model in the pediatric ICU. We developed and evaluated ML algorithms for the early prediction of hypothermia in a pediatric ICU. Sepsis advanced forecasting engine ICU Database (SafeICU) data resource is an in-house ICU source of data built in the Pediatric ICU at the All-India Institute of Medical Science (AIIMS), New Delhi. Each time-stamp at 1-min resolution was labeled for the presence of hypothermia to construct a retrospective cohort of pediatric patients in the SafeICU data resource. The training set consisted of windows of the length of 4.2 h with a lead time of 30 min-4 h from the onset of hypothermia. A set of 3,835 hand-engineered time-series features were calculated to capture physiological features from the time series. Features selection using the Boruta algorithm was performed to select the most important predictors of hypothermia. A battery of models such as gradient boosting machine, random forest, AdaBoost, and support vector machine (SVM) was evaluated utilizing five-fold test sets. The best-performing model was prospectively validated. A total of 148 patients with 193 ICU stays were eligible for the model development cohort. Of 3,939 features, 726 were statistically significant in the Boruta analysis for the prediction of Hypothermia. The gradient boosting model performed best with an Area Under the Receiver Operating Characteristic curve (AUROC) of 85% (SD = 1.6) and a precision of 59.2% (SD = 8.8) for a 30-min lead time before the onset of Hypothermia onset. As expected, the model showed a decline in model performance at higher lead times, such as AUROC of 77.2% (SD = 2.3) and precision of 41.34% (SD = 4.8) for 4 h ahead of Hypothermia onset. Our GBM(gradient boosting machine) model produced equal and superior results for the prospective validation, where an AUROC of 79.8% and a precision of 53% for a 30-min lead time before the onset of Hypothermia whereas an AUROC of 69.6% and a precision of 38.52% for a (30 min-4 h) lead time prospective validation of Hypothermia. Therefore, this work establishes a pipeline termed ThermoGnose for predicting hypothermia, a major complication in pediatric ICUs.
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Affiliation(s)
- Pradeep Singh
- Indraprastha Institute of Information Technology, Delhi, India
| | - Aditya Nagori
- Indraprastha Institute of Information Technology, Delhi, India
- CSIR-Institute of Genomics and Integrative Biology, New Delhi, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Rakesh Lodha
- All India Institute of Medical Sciences, Department of Pediatrics, New Delhi, India
| | - Tavpritesh Sethi
- Indraprastha Institute of Information Technology, Delhi, India
- All India Institute of Medical Sciences, Department of Pediatrics, New Delhi, India
- *Correspondence: Tavpritesh Sethi,
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18
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Nawaz A, Abbas Y, Ahmad T, Mahmoud NF, Rizwan A, Samee NA. A Healthcare Paradigm for Deriving Knowledge Using Online Consumers' Feedback. Healthcare (Basel) 2022; 10:1592. [PMID: 36011249 PMCID: PMC9407698 DOI: 10.3390/healthcare10081592] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 08/15/2022] [Accepted: 08/17/2022] [Indexed: 12/20/2022] Open
Abstract
Home healthcare agencies (HHCAs) provide clinical care and rehabilitation services to patients in their own homes. The organization's rules regulate several connected practitioners, doctors, and licensed skilled nurses. Frequently, it monitors a physician or licensed nurse for the facilities and keeps track of the health histories of all clients. HHCAs' quality of care is evaluated using Medicare's star ratings for in-home healthcare agencies. The advent of technology has extensively evolved our living style. Online businesses' ratings and reviews are the best representatives of organizations' trust, services, quality, and ethics. Using data mining techniques to analyze HHCAs' data can help to develop an effective framework for evaluating the finest home healthcare facilities. As a result, we developed an automated predictive framework for obtaining knowledge from patients' feedback using a combination of statistical and machine learning techniques. HHCAs' data contain twelve performance characteristics that we are the first to analyze and depict. After adequate pattern recognition, we applied binary and multi-class approaches on similar data with variations in the target class. Four prominent machine learning models were considered: SVM, Decision Tree, Random Forest, and Deep Neural Networks. In the binary class, the Deep Neural Network model presented promising performance with an accuracy of 97.37%. However, in the case of multiple class, the random forest model showed a significant outcome with an accuracy of 91.87%. Additionally, variable significance is derived from investigating each attribute's importance in predictive model building. The implications of this study can support various stakeholders, including public agencies, quality measurement, healthcare inspectors, and HHCAs, to boost their performance. Thus, the proposed framework is not only useful for putting valuable insights into action, but it can also help with decision-making.
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Affiliation(s)
- Aftab Nawaz
- Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock 43600, Pakistan
| | - Yawar Abbas
- Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock 43600, Pakistan
| | - Tahir Ahmad
- Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock 43600, Pakistan
| | - Noha F. Mahmoud
- Department of Rehabilitation Sciences, College of Health and Rehabilitation Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Atif Rizwan
- Department of Computer Engineering, Jeju National University, Jejusi 63243, Korea
| | - Nagwan Abdel Samee
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
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19
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Goodwin AJ, Eytan D, Dixon W, Goodfellow SD, Doherty Z, Greer RW, McEwan A, Tracy M, Laussen PC, Assadi A, Mazwi M. Timing errors and temporal uncertainty in clinical databases-A narrative review. Front Digit Health 2022; 4:932599. [PMID: 36060541 PMCID: PMC9433547 DOI: 10.3389/fdgth.2022.932599] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 07/11/2022] [Indexed: 11/28/2022] Open
Abstract
A firm concept of time is essential for establishing causality in a clinical setting. Review of critical incidents and generation of study hypotheses require a robust understanding of the sequence of events but conducting such work can be problematic when timestamps are recorded by independent and unsynchronized clocks. Most clinical models implicitly assume that timestamps have been measured accurately and precisely, but this custom will need to be re-evaluated if our algorithms and models are to make meaningful use of higher frequency physiological data sources. In this narrative review we explore factors that can result in timestamps being erroneously recorded in a clinical setting, with particular focus on systems that may be present in a critical care unit. We discuss how clocks, medical devices, data storage systems, algorithmic effects, human factors, and other external systems may affect the accuracy and precision of recorded timestamps. The concept of temporal uncertainty is introduced, and a holistic approach to timing accuracy, precision, and uncertainty is proposed. This quantitative approach to modeling temporal uncertainty provides a basis to achieve enhanced model generalizability and improved analytical outcomes.
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Affiliation(s)
- Andrew J. Goodwin
- Department of Critical Care Medicine, The Hospital for Sick Children, Toronto, ON, Canada
- School of Biomedical Engineering, University of Sydney, Sydney, NSW, Australia
| | - Danny Eytan
- Department of Critical Care Medicine, The Hospital for Sick Children, Toronto, ON, Canada
- Department of Medicine, Technion - Israel Institute of Technology, Haifa, Israel
| | - William Dixon
- Department of Critical Care Medicine, The Hospital for Sick Children, Toronto, ON, Canada
| | - Sebastian D. Goodfellow
- Department of Critical Care Medicine, The Hospital for Sick Children, Toronto, ON, Canada
- Department of Civil and Mineral Engineering, University of Toronto, Toronto, ON, Canada
| | - Zakary Doherty
- Research Fellow, School of Rural Health, Monash University, Melbourne, VIC, Australia
| | - Robert W. Greer
- Department of Critical Care Medicine, The Hospital for Sick Children, Toronto, ON, Canada
| | - Alistair McEwan
- School of Biomedical Engineering, University of Sydney, Sydney, NSW, Australia
| | - Mark Tracy
- Neonatal Intensive Care Unit, Westmead Hospital, Sydney, NSW, Australia
- Department of Paediatrics and Child Health, The University of Sydney, Sydney, NSW, Australia
| | - Peter C. Laussen
- Department of Anesthesia, Boston Children's Hospital, Boston, MA, United States
| | - Azadeh Assadi
- Department of Critical Care Medicine, The Hospital for Sick Children, Toronto, ON, Canada
- Department of Engineering and Applied Sciences, Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Mjaye Mazwi
- Department of Critical Care Medicine, The Hospital for Sick Children, Toronto, ON, Canada
- Department of Paediatrics, University of Toronto, Toronto, ON, Canada
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20
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Thakre TP, Kulkarni H, Adams KS, Mischel R, Hayes R, Pandurangi A. Polysomnographic identification of anxiety and depression using deep learning. J Psychiatr Res 2022; 150:54-63. [PMID: 35358832 DOI: 10.1016/j.jpsychires.2022.03.027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 03/10/2022] [Accepted: 03/21/2022] [Indexed: 10/18/2022]
Abstract
Anxiety and depression are common psychiatric conditions associated with significant morbidity and healthcare costs. Sleep is an evolutionarily conserved health state. Anxiety and depression have a bidirectional relationship with sleep. This study reports on the use of analysis of polysomnographic data using deep learning methods to detect the presence of anxiety and depression. Polysomnography data on 940 patients performed at an academic sleep center during the 3-year period from 01/01/2016 to 12/31/2018 were identified for analysis. The data were divided into 3 subgroups: 205 patients with Anxiety/Depression, 349 patients with no Anxiety/Depression, and 386 patients with likely Anxiety/Depression. The first two subgroups were used for training and testing of the deep learning algorithm, and the third subgroup was used for external validation of the resulting model. Hypnograms were constructed via automatic sleep staging, with the 12-channel PSG data being transformed into three-channel RGB (red, green, blue channels) images for analysis. Composite patient images were generated and utilized for training the Xception model, which provided a validation set accuracy of 0.9782 on the ninth training epoch. In the independent test set, the model achieved a high accuracy (0.9688), precision (0.9533), recall (0.9630), and F1-score (0.9581). Classification performance of most other mainstream deep learning models was comparable. These findings suggest that machine learning techniques have the potential to accurately detect the presence of anxiety and depression from analysis of sleep study data. Further studies are needed to explore the utility of these techniques in the field of psychiatry.
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Affiliation(s)
- Tushar P Thakre
- Department of Psychiatry, Virginia Commonwealth University School of Medicine, Richmond, VA, USA; Center for Sleep Medicine, Virginia Commonwealth University Health, Richmond, VA, USA
| | | | - Katie S Adams
- Department of Psychiatry, Virginia Commonwealth University School of Medicine, Richmond, VA, USA; Department of Pharmacy Services, Virginia Commonwealth University Health, Richmond, VA, USA
| | - Ryan Mischel
- Department of Psychiatry, Washington University at St. Louis School of Medicine, St. Louis, MO, USA
| | - Ronnie Hayes
- Center for Sleep Medicine, Virginia Commonwealth University Health, Richmond, VA, USA
| | - Ananda Pandurangi
- Department of Psychiatry, Virginia Commonwealth University School of Medicine, Richmond, VA, USA
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21
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Fritsch S, Maassen O, Riedel M. [Artificial Intelligence: Infrastructures and Prerequisites at European Level]. Anasthesiol Intensivmed Notfallmed Schmerzther 2022; 57:172-184. [PMID: 35320840 DOI: 10.1055/a-1423-8052] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
The application of artificial intelligence (AI) is often associated with the use of large amounts of data for the construction of AI models and algorithms. This data should ideally comply with the FAIR Data principles, i.e. being findable, accessible, interoperable and reusable. However, the handling of health data poses a particular challenge in this context. In this article, we highlight the challenges of the data usage for AI in medicine using the example of anaesthesia and intensive care medicine. We discuss the current situation but also the obstacles for a wider application of AI in medicine in Europe and give suggestions how to solve the different issues. The article covers different subjects like data protection, research data infrastructures and approval of medical products. Finally, this article shows how it can nevertheless be possible to establish a secure and at the same time effective handling of data for use in AI at the European level despite its unneglectable difficulties.
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22
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Variane GFT, Camargo JPV, Rodrigues DP, Magalhães M, Mimica MJ. Current Status and Future Directions of Neuromonitoring With Emerging Technologies in Neonatal Care. Front Pediatr 2022; 9:755144. [PMID: 35402367 PMCID: PMC8984110 DOI: 10.3389/fped.2021.755144] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Accepted: 11/18/2021] [Indexed: 11/13/2022] Open
Abstract
Neonatology has experienced a significant reduction in mortality rates of the preterm population and critically ill infants over the last few decades. Now, the emphasis is directed toward improving long-term neurodevelopmental outcomes and quality of life. Brain-focused care has emerged as a necessity. The creation of neonatal neurocritical care units, or Neuro-NICUs, provides strategies to reduce brain injury using standardized clinical protocols, methodologies, and provider education and training. Bedside neuromonitoring has dramatically improved our ability to provide assessment of newborns at high risk. Non-invasive tools, such as continuous electroencephalography (cEEG), amplitude-integrated electroencephalography (aEEG), and near-infrared spectroscopy (NIRS), allow screening for seizures and continuous evaluation of brain function and cerebral oxygenation at the bedside. Extended and combined uses of these techniques, also described as multimodal monitoring, may allow practitioners to better understand the physiology of critically ill neonates. Furthermore, the rapid growth of technology in the Neuro-NICU, along with the increasing use of telemedicine and artificial intelligence with improved data mining techniques and machine learning (ML), has the potential to vastly improve decision-making processes and positively impact outcomes. This article will cover the current applications of neuromonitoring in the Neuro-NICU, recent advances, potential pitfalls, and future perspectives in this field.
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Affiliation(s)
- Gabriel Fernando Todeschi Variane
- Division of Neonatology, Department of Pediatrics, Irmandade de Misericordia da Santa Casa de São Paulo, São Paulo, Brazil
- Clinical Research Department, Protecting Brains and Saving Futures Organization, São Paulo, Brazil
- Division of Neonatology, Grupo Santa Joana, São Paulo, Brazil
| | - João Paulo Vasques Camargo
- Clinical Research Department, Protecting Brains and Saving Futures Organization, São Paulo, Brazil
- Data Science Department, OPD Team, São Paulo, Brazil
| | - Daniela Pereira Rodrigues
- Clinical Research Department, Protecting Brains and Saving Futures Organization, São Paulo, Brazil
- Pediatric Nursing Department, Escola Paulista de Enfermagem, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Maurício Magalhães
- Division of Neonatology, Department of Pediatrics, Irmandade de Misericordia da Santa Casa de São Paulo, São Paulo, Brazil
- Clinical Research Department, Protecting Brains and Saving Futures Organization, São Paulo, Brazil
- Department of Pediatrics, Faculdade de Ciências Médicas da Santa Casa de São Paulo, São Paulo, Brazil
| | - Marcelo Jenné Mimica
- Department of Pathology, Faculdade de Ciências Médicas da Santa Casa de São Paulo, São Paulo, Brazil
- Department of Pediatrics, Irmandade da Santa Casa de Misericórdia de São Paulo, São Paulo, Brazil
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23
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Abiodun TN, Okunbor D, Chukwudi Osamor V. Remote Health Monitoring in Clinical Trial using Machine Learning Techniques: A Conceptual Framework. HEALTH AND TECHNOLOGY 2022; 12:359-364. [PMID: 35308032 PMCID: PMC8916791 DOI: 10.1007/s12553-022-00652-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 02/12/2022] [Accepted: 02/23/2022] [Indexed: 11/05/2022]
Abstract
Monitoring any process is crucial and very necessary, this is to ensure that standard protocols and procedures are strictly adhered to, monitoring clinical trials is not an exception. It is one of the most crucial processes that should be monitored because human subjects are involved. In trying to monitor clinical trial, information and communication technology techniques can be deployed to facilitate the process and hence improve accuracy. This research formulates a new conceptual framework for monitoring clinical trial using Support Vector Machine and Artificial Neural Network classifiers with physiological datasets from a wearable device. The proposed framework prototype consists of data collection module, data transmission module, and data analysis and prediction module. The data analytic and prediction module is the core section of the proposed framework tailored with data analysis. These datasets are preprocessed and transformed and then used to train and test the system, through different experimental analysis including bagging Support Vector Machine (SVM) and Artificial Neural Network (ANN). The outcome of the analysis presents classification into three different categories, such as fit, unfit, and undecided participants. These various classifications are used to determine if a participant should be allowed to continue in the trial or not. This research provides a framework that is useful in monitoring clinical trial remotely, thereby informing the decision-making process of the research team.
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Mollura M, Lehman LWH, Mark RG, Barbieri R. A novel artificial intelligence based intensive care unit monitoring system: using physiological waveforms to identify sepsis. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2021; 379:20200252. [PMID: 34689614 PMCID: PMC8805602 DOI: 10.1098/rsta.2020.0252] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/16/2021] [Indexed: 05/02/2023]
Abstract
A massive amount of multimodal data are continuously collected in the intensive care unit (ICU) along each patient stay, offering a great opportunity for the development of smart monitoring devices based on artificial intelligence (AI). The two main sources of relevant information collected in the ICU are the electronic health records (EHRs) and vital sign waveforms continuously recorded at the bedside. While EHRs are already widely processed by AI algorithms for prompt diagnosis and prognosis, AI-based assessments of the patients' pathophysiological state using waveforms are less developed, and their use is still limited to real-time monitoring for basic visual vital sign feedback at the bedside. This study uses data from the MIMIC-III database (PhysioNet) to propose a novel AI approach in ICU patient monitoring that incorporates features estimated by a closed-loop cardiovascular model, with the specific goal of identifying sepsis within the first hour of admission. Our top benchmark results (AUROC = 0.92, AUPRC = 0.90) suggest that features derived by cardiovascular control models may play a key role in identifying sepsis, by continuous monitoring performed through advanced multivariate modelling of vital sign waveforms. This work lays foundations for a deeper data integration paradigm which will help clinicians in their decision-making processes. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.
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Affiliation(s)
- Maximiliano Mollura
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Li-Wei H. Lehman
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Roger G. Mark
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Riccardo Barbieri
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy
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Hypotension Prediction Index with non-invasive continuous arterial pressure waveforms (ClearSight): clinical performance in Gynaecologic Oncologic Surgery. J Clin Monit Comput 2021; 36:1325-1332. [PMID: 34618291 PMCID: PMC8496438 DOI: 10.1007/s10877-021-00763-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Accepted: 09/29/2021] [Indexed: 12/23/2022]
Abstract
Intraoperative hypotension (IOH) is common during major surgery and is associated with a poor postoperative outcome. Hypotension Prediction Index (HPI) is an algorithm derived from machine learning that uses the arterial waveform to predict IOH. The aim of this study was to assess the diagnostic ability of HPI working with non-invasive ClearSight system in predicting impending hypotension in patients undergoing major gynaecologic oncologic surgery (GOS). In this retrospective analysis hemodynamic data were downloaded from an Edwards Lifesciences HemoSphere platform and analysed. Receiver operating characteristic curves were constructed to evaluate the performance of HPI working on the ClearSight pressure waveform in predicting hypotensive events, defined as mean arterial pressure < 65 mmHg for > 1 min. Sensitivity, specificity, positive predictive value and negative predictive value were computed at a cutpoint (the value which minimizes the difference between sensitivity and specificity). Thirty-one patients undergoing GOS were included in the analysis, 28 of which had complete data set. The HPI predicted hypotensive events with a sensitivity of 0.85 [95% confidence interval (CI) 0.73-0.94] and specificity of 0.85 (95% CI 0.74-0.95) 15 min before the event [area under the curve (AUC) 0.95 (95% CI 0.89-0.99)]; with a sensitivity of 0.82 (95% CI 0.71-0.92) and specificity of 0.83 (95% CI 0.71-0.93) 10 min before the event [AUC 0.9 (95% CI 0.83-0.97)]; and with a sensitivity of 0.86 (95% CI 0.78-0.93) and specificity 0.86 (95% CI 0.77-0.94) 5 min before the event [AUC 0.93 (95% CI 0.89-0.97)]. HPI provides accurate and continuous prediction of impending IOH before its occurrence in patients undergoing GOS in general anesthesia.
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Machine Learning-Based Data-Driven Fault Detection/Diagnosis of Lithium-Ion Battery: A Critical Review. ELECTRONICS 2021. [DOI: 10.3390/electronics10111309] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Fault detection/diagnosis has become a crucial function of the battery management system (BMS) due to the increasing application of lithium-ion batteries (LIBs) in highly sophisticated and high-power applications to ensure the safe and reliable operation of the system. The application of Machine Learning (ML) in the BMS of LIB has long been adopted for efficient, reliable, accurate prediction of several important states of LIB such as state of charge, state of health and remaining useful life. Inspired by some of the promising features of ML-based techniques over the conventional LIB fault detection/diagnosis methods such as model-based, knowledge-based and signal processing-based techniques, ML-based data-driven methods have been a prime research focus in the last few years. This paper provides a comprehensive review exclusively on the state-of-the-art ML-based data-driven fault detection/diagnosis techniques to provide a ready reference and direction to the research community aiming towards developing an accurate, reliable, adaptive and easy to implement fault diagnosis strategy for the LIB system. Current issues of existing strategies and future challenges of LIB fault diagnosis are also explained for better understanding and guidance.
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Maassen O, Fritsch S, Palm J, Deffge S, Kunze J, Marx G, Riedel M, Schuppert A, Bickenbach J. Future Medical Artificial Intelligence Application Requirements and Expectations of Physicians in German University Hospitals: Web-Based Survey. J Med Internet Res 2021; 23:e26646. [PMID: 33666563 PMCID: PMC7980122 DOI: 10.2196/26646] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Revised: 01/29/2021] [Accepted: 02/15/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND The increasing development of artificial intelligence (AI) systems in medicine driven by researchers and entrepreneurs goes along with enormous expectations for medical care advancement. AI might change the clinical practice of physicians from almost all medical disciplines and in most areas of health care. While expectations for AI in medicine are high, practical implementations of AI for clinical practice are still scarce in Germany. Moreover, physicians' requirements and expectations of AI in medicine and their opinion on the usage of anonymized patient data for clinical and biomedical research have not been investigated widely in German university hospitals. OBJECTIVE This study aimed to evaluate physicians' requirements and expectations of AI in medicine and their opinion on the secondary usage of patient data for (bio)medical research (eg, for the development of machine learning algorithms) in university hospitals in Germany. METHODS A web-based survey was conducted addressing physicians of all medical disciplines in 8 German university hospitals. Answers were given using Likert scales and general demographic responses. Physicians were asked to participate locally via email in the respective hospitals. RESULTS The online survey was completed by 303 physicians (female: 121/303, 39.9%; male: 173/303, 57.1%; no response: 9/303, 3.0%) from a wide range of medical disciplines and work experience levels. Most respondents either had a positive (130/303, 42.9%) or a very positive attitude (82/303, 27.1%) towards AI in medicine. There was a significant association between the personal rating of AI in medicine and the self-reported technical affinity level (H4=48.3, P<.001). A vast majority of physicians expected the future of medicine to be a mix of human and artificial intelligence (273/303, 90.1%) but also requested a scientific evaluation before the routine implementation of AI-based systems (276/303, 91.1%). Physicians were most optimistic that AI applications would identify drug interactions (280/303, 92.4%) to improve patient care substantially but were quite reserved regarding AI-supported diagnosis of psychiatric diseases (62/303, 20.5%). Of the respondents, 82.5% (250/303) agreed that there should be open access to anonymized patient databases for medical and biomedical research. CONCLUSIONS Physicians in stationary patient care in German university hospitals show a generally positive attitude towards using most AI applications in medicine. Along with this optimism comes several expectations and hopes that AI will assist physicians in clinical decision making. Especially in fields of medicine where huge amounts of data are processed (eg, imaging procedures in radiology and pathology) or data are collected continuously (eg, cardiology and intensive care medicine), physicians' expectations of AI to substantially improve future patient care are high. In the study, the greatest potential was seen in the application of AI for the identification of drug interactions, assumedly due to the rising complexity of drug administration to polymorbid, polypharmacy patients. However, for the practical usage of AI in health care, regulatory and organizational challenges still have to be mastered.
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Affiliation(s)
- Oliver Maassen
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
| | - Sebastian Fritsch
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, Germany
| | - Julia Palm
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Institute of Medical Statistics, Computer and Data Sciences, Jena University Hospital, Jena, Germany
| | - Saskia Deffge
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
| | - Julian Kunze
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
| | - Gernot Marx
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
| | - Morris Riedel
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, Germany
- School of Natural Sciences and Engineering, University of Iceland, Reykjavik, Iceland
| | - Andreas Schuppert
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Institute for Computational Biomedicine II, University Hospital RWTH Aachen, Aachen, Germany
| | - Johannes Bickenbach
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
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Giacobbe DR, Signori A, Del Puente F, Mora S, Carmisciano L, Briano F, Vena A, Ball L, Robba C, Pelosi P, Giacomini M, Bassetti M. Early Detection of Sepsis With Machine Learning Techniques: A Brief Clinical Perspective. Front Med (Lausanne) 2021; 8:617486. [PMID: 33644097 PMCID: PMC7906970 DOI: 10.3389/fmed.2021.617486] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 01/19/2021] [Indexed: 12/15/2022] Open
Abstract
Sepsis is a major cause of death worldwide. Over the past years, prediction of clinically relevant events through machine learning models has gained particular attention. In the present perspective, we provide a brief, clinician-oriented vision on the following relevant aspects concerning the use of machine learning predictive models for the early detection of sepsis in the daily practice: (i) the controversy of sepsis definition and its influence on the development of prediction models; (ii) the choice and availability of input features; (iii) the measure of the model performance, the output, and their usefulness in the clinical practice. The increasing involvement of artificial intelligence and machine learning in health care cannot be disregarded, despite important pitfalls that should be always carefully taken into consideration. In the long run, a rigorous multidisciplinary approach to enrich our understanding in the application of machine learning techniques for the early recognition of sepsis may show potential to augment medical decision-making when facing this heterogeneous and complex syndrome.
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Affiliation(s)
- Daniele Roberto Giacobbe
- Infectious Diseases Unit, San Martino Policlinico Hospital – IRCCS for Oncology and Neurosciences, Genoa, Italy
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Alessio Signori
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Filippo Del Puente
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Sara Mora
- Department of Informatics Bioengineering, Robotics, and Systems Engineering (DIBRIS), University of Genoa, Genoa, Italy
| | - Luca Carmisciano
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Federica Briano
- Infectious Diseases Unit, San Martino Policlinico Hospital – IRCCS for Oncology and Neurosciences, Genoa, Italy
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Antonio Vena
- Infectious Diseases Unit, San Martino Policlinico Hospital – IRCCS for Oncology and Neurosciences, Genoa, Italy
| | - Lorenzo Ball
- Anaesthesia and Intensive Care, San Martino Policlinico Hospital – IRCCS for Oncology and Neurosciences, Genoa, Italy
- Department of Surgical Sciences and Integrated Diagnostics (DISC), University of Genoa, Genoa, Italy
| | - Chiara Robba
- Anaesthesia and Intensive Care, San Martino Policlinico Hospital – IRCCS for Oncology and Neurosciences, Genoa, Italy
- Department of Surgical Sciences and Integrated Diagnostics (DISC), University of Genoa, Genoa, Italy
| | - Paolo Pelosi
- Anaesthesia and Intensive Care, San Martino Policlinico Hospital – IRCCS for Oncology and Neurosciences, Genoa, Italy
- Department of Surgical Sciences and Integrated Diagnostics (DISC), University of Genoa, Genoa, Italy
| | - Mauro Giacomini
- Department of Informatics Bioengineering, Robotics, and Systems Engineering (DIBRIS), University of Genoa, Genoa, Italy
| | - Matteo Bassetti
- Infectious Diseases Unit, San Martino Policlinico Hospital – IRCCS for Oncology and Neurosciences, Genoa, Italy
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
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Artificial intelligence in telemetry: what clinicians should know. Intensive Care Med 2021; 47:150-153. [PMID: 33386857 PMCID: PMC7776290 DOI: 10.1007/s00134-020-06295-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 10/12/2020] [Indexed: 12/19/2022]
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Simpao AF, Nelson O, Ahumada LM. Automated anesthesia artifact analysis: can machines be trained to take out the garbage? J Clin Monit Comput 2020; 35:225-227. [PMID: 32920701 DOI: 10.1007/s10877-020-00589-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 09/08/2020] [Indexed: 11/24/2022]
Affiliation(s)
- Allan F Simpao
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA, 19104-4399, USA.
- Department of Anesthesiology and Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA.
| | - Olivia Nelson
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA, 19104-4399, USA
- Department of Anesthesiology and Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Luis M Ahumada
- Department of Health Informatics, Predictive Analytics Core, Johns Hopkins All Children's Hospital, 501 6th Ave S, St. Petersburg, FL, 33701, USA
- Department of Anesthesiology and Critical Care Medicine, Johns Hopkins School of Medicine, 733 N Broadway, Baltimore, MD, 21205, USA
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Saugel B, Critchley LAH, Kaufmann T, Flick M, Kouz K, Vistisen ST, Scheeren TWL. Journal of Clinical Monitoring and Computing end of year summary 2019: hemodynamic monitoring and management. J Clin Monit Comput 2020; 34:207-219. [PMID: 32170569 PMCID: PMC7080677 DOI: 10.1007/s10877-020-00496-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Accepted: 03/05/2020] [Indexed: 12/27/2022]
Affiliation(s)
- Bernd Saugel
- Department of Anesthesiology, Center of Anesthesiology and Intensive Care Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,Outcomes Research Consortium, Cleveland, OH, USA
| | - Lester A H Critchley
- Department of Anesthesia and Intensive Care, The Chinese University of Hong Kong, Shatin, Hong Kong.,The Belford Hospital, Fort William, The Highlands, Scotland, UK
| | - Thomas Kaufmann
- Department of Anesthesiology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9700 RB, Groningen, The Netherlands
| | - Moritz Flick
- Department of Anesthesiology, Center of Anesthesiology and Intensive Care Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Karim Kouz
- Department of Anesthesiology, Center of Anesthesiology and Intensive Care Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Simon T Vistisen
- Department of Anaesthesia and Intensive Care, Aarhus University, Aarhus, Denmark
| | - Thomas W L Scheeren
- Department of Anesthesiology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9700 RB, Groningen, The Netherlands.
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Bashford J, Wickham A, Iniesta R, Drakakis E, Boutelle M, Mills K, Shaw CE. Preprocessing surface EMG data removes voluntary muscle activity and enhances SPiQE fasciculation analysis. Clin Neurophysiol 2019; 131:265-273. [PMID: 31740273 PMCID: PMC6941467 DOI: 10.1016/j.clinph.2019.09.015] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Revised: 09/03/2019] [Accepted: 09/23/2019] [Indexed: 12/11/2022]
Abstract
A novel preprocessing step removes the need for manual selection of relaxed surface EMG data. SPiQE provides reliable fasciculation analysis from raw thirty-minute recordings in ALS. This paves the way for clinical calibration of a potential novel biomarker of disease progression.
Objectives Fasciculations are a clinical hallmark of amyotrophic lateral sclerosis (ALS). The Surface Potential Quantification Engine (SPiQE) is a novel analytical tool to identify fasciculation potentials from high-density surface electromyography (HDSEMG). This method was accurate on relaxed recordings amidst fluctuating noise levels. To avoid time-consuming manual exclusion of voluntary muscle activity, we developed a method capable of rapidly excluding voluntary potentials and integrating with the established SPiQE pipeline. Methods Six ALS patients, one patient with benign fasciculation syndrome and one patient with multifocal motor neuropathy underwent monthly thirty-minute HDSEMG from biceps and gastrocnemius. In MATLAB, we developed and compared the performance of four Active Voluntary IDentification (AVID) strategies, producing a decision aid for optimal selection. Results Assessment of 601 one-minute recordings permitted the development of sensitive, specific and screening strategies to exclude voluntary potentials. Exclusion times (0.2–13.1 minutes), processing times (10.7–49.5 seconds) and fasciculation frequencies (27.4–71.1 per minute) for 165 thirty-minute recordings were compared. The overall median fasciculation frequency was 40.5 per minute (10.6–79.4 IQR). Conclusion We hereby introduce AVID as a flexible, targeted approach to exclude voluntary muscle activity from HDSEMG recordings. Significance Longitudinal quantification of fasciculations in ALS could provide unique insight into motor neuron health.
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Affiliation(s)
- J. Bashford
- UK Dementia Research Institute, Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, UK
- Corresponding author. https://spiqe.co.uk
| | - A. Wickham
- Department of Bioengineering, Imperial College London, UK
| | - R. Iniesta
- Department of Biostatistics and Health Informatics, King’s College London, UK
| | - E. Drakakis
- Department of Bioengineering, Imperial College London, UK
| | - M. Boutelle
- Department of Bioengineering, Imperial College London, UK
| | - K. Mills
- UK Dementia Research Institute, Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, UK
| | - CE. Shaw
- UK Dementia Research Institute, Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, UK
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Crews BO, Drees JC, Greene DN. Data-driven quality assurance to prevent erroneous test results. Crit Rev Clin Lab Sci 2019:1-15. [PMID: 31680585 DOI: 10.1080/10408363.2019.1678567] [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: 10/25/2022]
Abstract
Increasing laboratory automation and efficiency requires quality assurance (QA) approaches to ensure that reported results are precise and accurate. Prerequisites for designing optimal QA strategies include an in-depth understanding of the laboratory processes, the expected results, and of the mechanisms that can cause erroneous results. Oftentimes, a laboratory's own data, extracted from the laboratory information system, electronic medical record, and/or clinical data warehouse are necessary to master the aforementioned requirements. Data-driven QA utilizes retrospective and/or prospective laboratory results to minimize errors in the clinical laboratory due to pre-analytical or analytical vulnerabilities. Additionally, exploitation of this data may improve result interpretation. The objective of this review is to illustrate specific examples of data-driven QA approaches for several areas of the clinical laboratory and for different phases of the testing cycle.
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Affiliation(s)
- Bridgit O Crews
- Department of Pathology and Laboratory Medicine, University of California Irvine, Irvine, CA, USA
| | - Julia C Drees
- The Permanente Medical Group, Kaiser Permanente Northern California Regional Laboratories, Berkeley, CA, USA
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Celi LA, Fine B, Stone DJ. An awakening in medicine: the partnership of humanity and intelligent machines. Lancet Digit Health 2019; 1:e255-e257. [PMID: 32617524 PMCID: PMC7331949 DOI: 10.1016/s2589-7500(19)30127-x] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Affiliation(s)
- Leo Anthony Celi
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA (LAC); Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA (LAC); Department of Diagnostic Imaging and Operational Analytics Lab, Trillium Health Partners, Mississauga, ON, Canada (BF); Department of Medical Imaging, University of Toronto, ON, Canada (BF); Departments of Anesthesiology and Neurosurgery and the Center for Advanced Data Analytics, University of Virginia, Charlottesville, USA (DJS)
| | - Benjamin Fine
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA (LAC); Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA (LAC); Department of Diagnostic Imaging and Operational Analytics Lab, Trillium Health Partners, Mississauga, ON, Canada (BF); Department of Medical Imaging, University of Toronto, ON, Canada (BF); Departments of Anesthesiology and Neurosurgery and the Center for Advanced Data Analytics, University of Virginia, Charlottesville, USA (DJS)
| | - David J Stone
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA (LAC); Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA (LAC); Department of Diagnostic Imaging and Operational Analytics Lab, Trillium Health Partners, Mississauga, ON, Canada (BF); Department of Medical Imaging, University of Toronto, ON, Canada (BF); Departments of Anesthesiology and Neurosurgery and the Center for Advanced Data Analytics, University of Virginia, Charlottesville, USA (DJS)
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Towards development of alert thresholds for clinical deterioration using continuous predictive analytics monitoring. J Clin Monit Comput 2019; 34:797-804. [PMID: 31327101 DOI: 10.1007/s10877-019-00361-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2019] [Accepted: 07/16/2019] [Indexed: 10/26/2022]
Abstract
Patients who deteriorate while on the acute care ward and are emergently transferred to the Intensive Care Unit (ICU) experience high rates of mortality. To date, risk scores for clinical deterioration applied to the acute care wards rely on static or intermittent inputs of vital sign and assessment parameters. We propose the use of continuous predictive analytics monitoring, or data that relies on real-time physiologic monitoring data captured from ECG, documented vital signs, laboratory results, and other clinical assessments to predict clinical deterioration. A necessary step in translation to practice is understanding how an alert threshold would perform if applied to a continuous predictive analytic that was trained to detect clinical deterioration. The purpose of this study was to evaluate the positive predictive value of 'risk spikes', or large abrupt increases in the output of a statistical model of risk predicting clinical deterioration. We studied 8111 consecutive patient admissions to a cardiovascular medicine and surgery ward with continuous ECG data. We first trained a multivariable logistic regression model for emergent ICU transfer in a test set and tested the characteristics of the model in a validation set of 4059 patient admissions. Then, in a nested analysis we identified large, abrupt spikes in risk (increase by three units over the prior 6 h; a unit is the fold-increase in risk of ICU transfer in the next 24 h) and reviewed hospital records of 91 patients for clinical events such as emergent ICU transfer. We compared results to 59 control patients at times when they were matched for baseline risk including the National Warning Score (NEWS). There was a 3.4-fold higher event rate for patients with risk spikes (positive predictive value 24% compared to 7%, p = 0.006). If we were to use risk spikes as an alert, they would fire about once per day on a 73-bed acute care ward. Risk spikes that were primarily driven by respiratory changes (ECG-derived respiration (EDR) or charted respiratory rate) had highest PPV (30-35%) while risk spikes driven by heart rate had the lowest (7%). Alert thresholds derived from continuous predictive analytics monitoring are able to be operationalized as a degree of change from the person's own baseline rather than arbitrary threshold cut-points, which can likely better account for the individual's own inherent acuity levels. Point of care clinicians in the acute care ward settings need tailored alert strategies that promote a balance in recognition of clinical deterioration and assessment of the utility of the alert approach.
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Cosgriff CV, Celi LA, Stone DJ. Critical Care, Critical Data. Biomed Eng Comput Biol 2019; 10:1179597219856564. [PMID: 31217702 PMCID: PMC6563388 DOI: 10.1177/1179597219856564] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Accepted: 05/21/2019] [Indexed: 12/20/2022] Open
Abstract
As big data, machine learning, and artificial intelligence continue to penetrate into and transform many facets of our lives, we are witnessing the emergence of these powerful technologies within health care. The use and growth of these technologies has been contingent on the availability of reliable and usable data, a particularly robust resource in critical care medicine where continuous monitoring forms a key component of the infrastructure of care. The response to this opportunity has included the development of open databases for research and other purposes; the development of a collaborative form of clinical data science intended to fully leverage these data resources, and the creation of data-driven applications for purposes such as clinical decision support. Most recently, data levels have reached the thresholds required for the development of robust artificial intelligence features for clinical purposes. The systematic capture and analysis of clinical data in both individuals and populations allows us to begin to move toward precision medicine in the intensive care unit (ICU). In this perspective review, we examine the fundamental role of data as we present the current progress that has been made toward an artificial intelligence (AI)-supported, data-driven precision critical care medicine.
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Affiliation(s)
- Christopher V Cosgriff
- MIT Critical Data, Massachusetts Institute of Technology, Cambridge, MA, USA
- New York University School of Medicine, New York, NY, USA
| | - Leo Anthony Celi
- MIT Critical Data, Massachusetts Institute of Technology, Cambridge, MA, USA
- Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - David J Stone
- MIT Critical Data, Massachusetts Institute of Technology, Cambridge, MA, USA
- Departments of Anesthesiology and Neurosurgery, University of Virginia School of Medicine, Charlottesville, VA, USA
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Mancini M, Bloem BR, Horak FB, Lewis SJG, Nieuwboer A, Nonnekes J. Clinical and methodological challenges for assessing freezing of gait: Future perspectives. Mov Disord 2019; 34:783-790. [PMID: 31046191 DOI: 10.1002/mds.27709] [Citation(s) in RCA: 88] [Impact Index Per Article: 17.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Revised: 04/04/2019] [Accepted: 04/11/2019] [Indexed: 01/04/2023] Open
Abstract
Freezing of gait, defined as sudden and usually brief episodes of inability to produce effective stepping, often results in falls and is both disabling and common in parkinsonism. In this narrative review, sprung from the 2nd International Workshop on freezing of gait in Leuven, we summarize the latest insights into clinical and methodological challenges for assessing freezing of gait. We also highlight the role of emerging wearable technology to improve the management of this debilitating symptom. © 2019 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Martina Mancini
- Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA
| | - Bastiaan R Bloem
- Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour; Department of Neurology, Nijmegen, The Netherlands
| | - Fay B Horak
- Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA
| | - Simon J G Lewis
- Parkinson's Disease Research Clinic, Brain and Mind Centre, The University of Sydney, Camperdown, NSW, Australia
| | - Alice Nieuwboer
- Department of Rehabilitation Sciences, Katholieke Universiteit Leuven, Tervuursevest, Belgium
| | - Jorik Nonnekes
- Department of Rehabilitation, Radboud University Medical Centre; Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
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Intensive Care Unit Telemedicine in the Era of Big Data, Artificial Intelligence, and Computer Clinical Decision Support Systems. Crit Care Clin 2019; 35:483-495. [PMID: 31076048 DOI: 10.1016/j.ccc.2019.02.005] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
This article examines the history of the telemedicine intensive care unit (tele-ICU), the current state of clinical decision support systems (CDSS) in the tele-ICU, applications of machine learning (ML) algorithms to critical care, and opportunities to integrate ML with tele-ICU CDSS. The enormous quantities of data generated by tele-ICU systems is a major driver in the development of the large, comprehensive, heterogeneous, and granular data sets necessary to develop generalizable ML CDSS algorithms, and deidentification of these data sets expands opportunities for ML CDSS research.
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