Interpretable machine learning for predicting 30-day mortality following intracranial hemorrhage surgery

This study establishes an interpretable XGBoost model that leverages perioperative data to accurately predict short-term mortality after ICH surgery. By highlighting the prognostic value of these modifiable biomarkers, the model serves as a practical tool for early risk stratification, assisting in the optimization of perioperative management in critical care settings.