Development and validation of interpretable machine learning models for triage patients admitted to the intensive care unit.

ABSTRACT:

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 per

SUBMITTER: Liu Z 

PROVIDER: S-EPMC11835250 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature