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: The study analyzed 189,167 emergency patients from the Medical Information Mart for Intensive Care IV database, with the outcome being ICU admission. Three models were compared: Model 1 based on Emergency Severity Index (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), F1 Score, Positive Predictive Value, Negative Predictive Value, Brier score, calibration curves, and decision curves analysis were used to evaluate the performance of the models. SHapley Additive exPlanations was used for explaining ML models. Results: The AUC of Model 3 was superior to that of Model 1 and Model 2. In Model 3, the top four algorithms with the highest AUC were Gradient Boosting (0.81), Logistic Regression (0.81), naive Bayes (0.80), and Random Forest (0.80). Upon further comparison of the four algorithms, Gradient Boosting was slightly superior to Random Forest and Logistic Regression, while naive Bayes performed the worst. Conclusions: This study developed an interpretable ML triage model using vital signs, demographics, medical history, and chief complaints, proving more effective than traditional models in predicting ICU admission. Interpretable ML aids clinical decisions during triage.