What question did this study set out to answer?

The study aims to develop and validate machine learning models to predict mortality in ICU patients using electronic health records.

April 21, 2026Open Access

Development and Validation of a Multivariable Machine Learning Model for Mortality Prediction among Intensive Care Unit Patients

Key Points

The study aims to develop and validate machine learning models to predict mortality in ICU patients using electronic health records.
Performed a retrospective cohort analysis of 5,553 ICU admissions from September 2021 to December 2023.
Developed three predictive models: Logistic Regression, Random Forest, and XGBoost using demographic, APACHE II, SOFA values, comorbidities, and ventilatory support status.
Evaluated model performance via area under the receiver operating characteristic curve (AUROC), precision, recall, and F1 score.
1,869 patients experienced mortality, resulting in a 33.6% ICU mortality rate.
The Random Forest model achieved the highest AUROC of 0.842, followed by XGBoost at 0.835 and Logistic Regression at 0.833.
APACHE II and SOFA values were identified as the most significant predictors across all models.

Abstract

Background and aims: Precise forecasting of mortality in intensive care units (ICUs) is essential for enhancing patient management and resource distribution.Traditional scoring methods like the Acute Physiology and Chronic Health Evaluation II (APACHE II) and Sequential Organ Failure Assessment (SOFA) are prevalent yet may inadequately encompass the intricacies of critical illness.The aim of this work was to create and internally test machine learning models for predicting mortality in the ICU, utilizing routinely gathered electronic health record data.Patients and methods: This retrospective cohort analysis encompassed 5,553 adult ICU hospitalizations from September 2021 to December 2023.Patients were randomly allocated into development (80%) and test (20%) cohorts.Three predictive models-Logistic Regression, Random Forest, and Extreme Gradient Boosting (XGBoost)-were developed utilizing demographic information, APACHE II and SOFA values, comorbidities, and ventilatory support status.The evaluation of model performance was conducted utilizing the area under the receiver operating characteristic curve (AUROC), precision, recall, and F1 score.Results: Among the 5,553 analyzed ICU admissions, 1,869 patients succumbed during their ICU stay, yielding an ICU mortality rate of 33.6% (95% CI: 32.4-34.9).The Random Forest model had the superior discriminative capability for mortality prediction, achieving an AUROC of 0.842, followed by XGBoost with an AUROC of 0.835, and Logistic Regression with an AUROC of 0.833.Although Logistic Regression demonstrated slightly superior overall accuracy, ensemble models more effectively identified non-linear correlations among predictors.Acute Physiology and Chronic Health Evaluation II and SOFA values proved to be the most significant predictors in all models.Temporal validation and sensitivity analysis produced consistent outcomes, demonstrating the resilience of model performance.Conclusion: Machine learning models exhibited strong efficacy in predicting ICU mortality, with Random Forest and XGBoost displaying slight advantages over Logistic Regression.The incorporation of machine learning algorithms into existing ICU scoring systems may improve risk categorization and facilitate clinical decision-making.

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