Hybrid Explainable AI for Early Sepsis Prediction with Actionable Lead Time and Clinical Utility Validation
This study presents a hybrid explainable AI framework that integrates gradient boosting and survival analysis to achieve strong discrimination and calibration for early sepsis prediction, demonstrating significant clinical utility through actionable lead times and a favorable alert burden while identifying key physiologic instability markers.