Early mortality prediction after severe trauma using ensemble machine learning: a single-center retrospective study.

ABSTRACT: Early identification of trauma patients at risk of in-hospital death is essential for guiding time-critical resuscitation and operative strategies. We developed and evaluated a multi-model machine learning framework that integrates vital signs, and routine laboratory and blood-gas metrics indices obtained within 30 min of emergency department arrival to predict in-hospital mortality. This single-center retrospective study included 408 critically injured trauma patients treated at the Emergency Department of the Affiliated Kunshan Hospital of Jiangsu University (January 2020-December 2021). We implemented multiple machine learning models [logistic regression, Random Forest, Gradient Boosting, XGBoost, LightGBM, and multilayer perceptron (MLP)], and constructed stacking and soft-voting ensem

SUBMITTER: Ling L 

PROVIDER: S-EPMC12756457 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature