Development and Validation of Interpretable Machine Learning Models for Triage Patients Admitted to the Intensive Care Unit

Objectives: Developing and validating interpretable machine learning (ML) models for predicting whether triaged patients need to be admitted to the intensive care unit (ICU).Measures: This was a single-center, retrospective study. Emergency Severity Index (ESI), vital signs, demographic characteristics, history, and chief complaints of triaged patients were extracted from the Medical Information Mart for Intensive Care IV database, and the predicted outcome was admission to the ICU.Three models were compared: Model 1 based on ESI, Model 2 on vital signs, and Model 3 on vital signs, demographic characteristics, medical history, and chief complaints. Nine ML algorithms were employed. The area under the receiver operating characteristic curve (AUC), Matthews correlation coefficient (MCC), learning curves, recall curves, calibration curves, and decision curves analysis were used to evaluate the performance of the models.