Machine learning techniques for mortality prediction in emergency departments: a systematic review

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, artifi...