This systematic review aimed to assess the performance and clinical feasibility of ML algorithms in prediction of in-hospital mortality for medical patients using vital signs at emergency departments.

Design: A systematic review was performed.

Setting: The databases including Medline (PubMed), Scopus, and Embase (Ovid) were searched between 2010 and 2021, to extract published articles in English, describing ML-based models utilizing vital signs variables to predict in-hospital mortality for patients admitted at emergency departments. CHARMS checklist was used for study planning and data extraction. The risk of bias for included papers was assessed using the PROBAST tool.

Participants: Admitted patients to the ED

Main outcome measure: In-hospital mortality.

Results: Fifteen articles were included in the final review. We found that eight models including logistic regression, decision tree, K-nearest neighbors, support vector machine, gradient boosting, random forest, artificial neural networks, and deep neural networks have been applied in this domain. Most studies failed to report essential main analysis steps such as data preprocessing and handling missing values. Fourteen included studies had a high risk of bias in the statistical analysis part, which could lead to poor performance in practice. Although the main aim of all studies was developing a predictive model for mortality, nine articles did not provide a time horizon for the prediction.

Conclusion: This review provided an updated overview of the state-of-the-art and revealed research gaps; based on these, we provide eight recommendations for future studies to make the use of ML more feasible in practice. By following these recommendations, we expect to see more robust ML models applied in the future to help clinicians identify patient deterioration earlier.

Three databases including Medline (PubMed), Scopus, and Embase (Ovid) were targeted. Relevant articles were extracted using a broad range of relevant keywords. We stratified keywords into five groups namely, ML keywords, medical keywords, document type, publication year, and language. The keywords in a group were paired using OR operators and all groups were paired using AND operator.