An explainable knowledge distillation method with XGBoost for ICU mortality prediction

•Distilling knowledge from deep learning to improve predictive performance of XGBoost.•Model calibration for obtaining prediction reflecting the true posterior probabilities.•Insights about patients’ physiological conditions are obtained through SHAP.•Comprehensive experiments were conducted on the MIMIC-III dataset.