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A multicenter machine learning model for predicting ICU mortality in mechanically ventilated patients: development

Yi Zhang^1,2, Xu Chen², Xinghan Tian³

¹Respiratory and Critical Care Medicine Department, The Second Affiliated Hospital of Anhui Medical University, HeFei, AnHui province, China.

BMC Medical Informatics and Decision Making

|July 7, 2026

Summary

This study developed a machine learning model to predict intensive care unit (ICU) mortality in mechanically ventilated patients. The random forest model accurately identifies high-risk patients within 48 hours of admission.

Keywords:

External validation ICU mortality Machine learning Mechanical ventilation Random forest Risk prediction

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Area of Science:

Critical Care Medicine
Machine Learning in Healthcare
Predictive Analytics

Background:

Predicting mortality in mechanically ventilated ICU patients is challenging due to patient heterogeneity.
Traditional scoring systems lack flexibility and generalizability for early risk stratification.
Developing advanced models is crucial for timely clinical decision support.

Purpose of the Study:

To develop and externally validate a machine learning model for predicting ICU mortality.
To utilize routinely available clinical variables for accurate risk stratification.
To enhance early identification of high-risk mechanically ventilated patients.

Main Methods:

A retrospective multicenter cohort study using MIMIC, eICU, and an independent institutional cohort.
Feature selection via LASSO regression and recursive feature elimination.
Random forest model development and validation using discrimination, calibration, and SHAP analysis.

Main Results:

A 21-predictor random forest model demonstrated superior performance.
The model achieved favorable early discrimination (first 48 hours) in external validation.
Key predictors included age, respiratory dysfunction, metabolic stress, organ failure, and treatment intensity.

Conclusions:

The random forest model offers accurate, interpretable, admission-time risk stratification for ICU mortality.
It serves as a valuable triage and screening tool within the first 48 hours.
The model shows potential for early clinical decision support in critical care settings.