A machine learning model for predicting 28-day mortality in ICU patients with community-acquired pneumonia and acute kidney injury.

ABSTRACT: Acute kidney injury is a common and critical complication in patients with community-acquired pneumonia who are admitted to intensive care units, substantially increasing their risk of short-term mortality. To enhance early clinical decision-making, we developed and validated multiple machine learning-based survival models to predict 28-day mortality using data from the Medical Information Mart for Intensive Care (MIMIC IV and MIMIC III databases). Five models were evaluated: Random Survival Forests, Gradient Boosting Machine, Lasso-Cox, CoxBoost, and Survival-SVM. Among these, the CoxBoost model demonstrated superior predictive performance with an AUC of 0.737in internal validation cohort and an AUC of 0.671 in external validation cohort, outperforming established clinical scoring systems

SUBMITTER: Ji W 

PROVIDER: S-EPMC12689605 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature