Early prediction of transfusion requirements in trauma patients using explainable machine learning

XGBoost demonstrated strong predictive performance using only arrival-time variables, and SHAP explanations offered clinically intuitive reasoning. These characteristics highlight the model's potential as a rapid decision-support tool, although external validation remains an important next step.