Interpretable prediction of hospital mortality in bleeding critically ill patients based on machine learning and SHAP.

ABSTRACT:

Background

Hemorrhage is a prevalent and critical condition in the intensive care unit (ICU), characterized by high incidence, elevated mortality rates, and substantial therapeutic challenges. Accurate prediction of mortality in patients with hemorrhage is essential for developing personalized prevention and treatment strategies. Nevertheless, the implementation of effective predictive models in clinical practice remains limited, primarily due to the lack of robust and interpretable tools.

Objective

This study aimed to develop an interpretable model for predicting mortality risk in critically ill patients with hemorrhage admitted to ICUs. The SHapley Additive exPlanations (SHAP) method was applied to interpret the eXtreme Gradient Boosting (XGBoost)model, identifying key pro

SUBMITTER: Ren B 

PROVIDER: S-EPMC12261736 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature