Development of an interpretable machine learning model for predicting venous thromboembolism in intensive care unit patients with intracerebral hemorrhage

The XGBoost model accurately predicts VTE occurrence in ICH patients in the ICU. By employing the SHAP method, it is possible to precisely assess the impact of various pathophysiological parameters on individual patient predictions, thereby providing robust support for personalized risk stratification and preventive treatment.