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

Area of Science:

• Clinical Informatics
• Artificial Intelligence in Healthcare
• Patient Monitoring

Background:

• Early identification of high-risk patients is crucial for effective resource allocation and preventing adverse outcomes.
• Vital signs-based early warning systems (EWS) are standard for predicting cardiorespiratory instability and sepsis.
• Machine learning (ML) models offer advanced capabilities in analyzing vital sign trends for risk prediction.

Purpose of the Study:

• To systematically review and evaluate ML-based EWS using vital signs for predicting physiological deterioration in acute and ambulatory care.
• To summarize the current utility and identify challenges associated with these ML models.

Main Methods:

• A comprehensive search of multiple databases (PubMed, CINAHL, Cochrane, Web of Science, Embase, Google Scholar) was conducted.
• Peer-reviewed studies utilizing patient vital signs and demographics with ML models for outcome prediction were included.
• Data extraction followed PRISMA, TRIPOD, and Cochrane guidelines.

Main Results:

• 24 studies were included, predominantly retrospective (23/24), across diverse care settings.
• Common ML models included logistic regression, tree-based methods, kernel methods, and neural networks.
• Model performance, measured by AUC, ranged from 0.57 to 0.97.

Conclusions:

• ML-based EWS demonstrate potential for higher accuracy than aggregate-weighted systems.
• Standardized outcome measures are needed for rigorous performance evaluation.
• Future research should focus on model interpretability, clinical efficacy via prospective studies, and impact across settings.