The integration of advanced techniques with the XGBoost algorithm yielded a highly accurate and interpretable model for predicting Sepsis-Associated Acute Kidney Injury (SA-AKI) mortality across diverse populations.

It supports early identification of high-risk patients, enhancing clinical decision-making in intensive care.

Future work needs to focus on enhancing adaptability, versatility, and real-world applications.

For 9,474 identified SA-AKI patients in MIMIC-IV, key features like lab results, vital signs, and comorbidities were selected using Variance Inflation Factor (VIF), Recursive Feature Elimination (RFE), and expert input, narrowing to 24 predictive variables.

An Extreme Gradient Boosting (XGBoost) model was built for in-hospital mortality prediction, with hyperparameters optimized using GridSearch.

Model interpretability was enhanced with SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME).

External validation was conducted using the eICU database.

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