Machine Learning–Based Early Warning Systems for Clinical Deterioration: Systematic Scoping Review

Abstract

Background Timely identification of patients at a high risk of clinical deterioration is key to prioritizing care, allocating resources effectively, and preventing adverse outcomes. Vital signs–based, aggregate-weighted early warning systems are commonly used to predict the risk of outcomes related to cardiorespiratory instability and sepsis, which are strong predictors of poor outcomes and mortality. Machine learning models, which can incorporate trends and capture relationships among parameters that aggregate-weighted models cannot, have recently been showing promising results. Objective This study aimed to identify, summarize, and evaluate the available research, current state of utility, and challenges with machine learning–based early warning systems using vital signs to predict the risk of physiological deterioration in acutely ill patients, across acute and ambulatory care settings. Methods PubMed, CINAHL, Cochrane Library, Web of Science, Embase, and Google Scholar were searched for peer-reviewed, original studies with keywords related to “vital signs,” “clinical deterioration,” and “machine learning.” Included studies used patient vital signs along with demographics and described a machine learning model for predicting an outcome in acute and ambulatory care settings. Data were extracted following PRISMA, TRIPOD, and Cochrane Collaboration guidelines. Results We identified 24 peer-reviewed studies from 417 articles for inclusion; 23 studies were retrospective, while 1 was prospective in nature. Care settings included general wards, intensive care units, emergency departments, step-down units, medical assessment units, postanesthetic wards, and home care. Machine learning models including logistic regression, tree-based methods, kernel-based methods, and neural networks were most commonly used to predict the risk of deterioration. The area under the curve for models ranged from 0.57 to 0.97. Conclusions In studies that compared performance, reported results suggest that machine learning–based early warning systems can achieve greater accuracy than aggregate-weighted early warning systems but several areas for further research were identified. While these models have the potential to provide clinical decision support, there is a need for standardized outcome measures to allow for rigorous evaluation of performance across models. Further research needs to address the interpretability of model outputs by clinicians, clinical efficacy of these systems through prospective study design, and their potential impact in different clinical settings.

Machine Learning in Medical Emergencies: a Systematic Review and Analysis

“…Machine Learning-Based Early Warning Systems for Clinical Deterioration: Systematic Scoping Review [ 27 ]…”

User interface approaches implemented with automated patient deterioration surveillance tools: protocol for a scoping review

“…However, few studies assess the clinical benefits of these algorithms in the real world. 11 There are scoping reviews covering issues surrounding the development and implementation of ML decision support tools in general. However, those reviews have objectives that are different than this protocol.…”

Intensivmedizinisches Monitoring

“…Die Fortschritte in der Anwendung der künstlichen Intelligenz und des maschinellen Lernens könnten in der Zukunft Möglichkeiten öffnen, die in einem PDMS gespeicherten Monitoring-und Behandlungsdaten automatisiert auszuwerten und für Warnhinweise, Prognoseabschätzungen oder Therapieempfehlungen zu nutzen [26][27][28]. Welche Chancen und Gefahren in solchen Systemen liegen, die derzeit entwickelt und beginnend evaluiert werden, ist derzeit noch nicht abschätzbar, insbesondere wenn die zugrunde liegenden Entscheidungsalgorithmen nicht mehr nachvollziehbar sind.…”

“…Welche Chancen und Gefahren in solchen Systemen liegen, die derzeit entwickelt und beginnend evaluiert werden, ist derzeit noch nicht abschätzbar, insbesondere wenn die zugrunde liegenden Entscheidungsalgorithmen nicht mehr nachvollziehbar sind. Trotzdem sind eine Reihe von Szenarien denkbar, in denen ein CDSS die ärztliche Entscheidung unterstützen könnte [26][27][28].…”