Data Sheet 1_Stage prediction of acute kidney injury in sepsis patients using explainable machine learning approaches.docx

BackgroundAcute kidney injury (AKI) is a prevalent and serious complication among sepsis patients, closely associated with high mortality rates and substantial disease burden. Early prediction of AKI is vital for prompt and effective intervention and improved prognosis. This research seeks to construct and assess forecasting frameworks that leverage advanced machine learning algorithms to anticipate AKI progression in high-risk sepsis patients.
MethodsThis study utilized the MIMIC-IV database, a large, publicly available critical care dataset containing comprehensive, de-identified electronic health records of over 70,000 ICU admissions at Beth Israel Deaconess Medical Center, to extract sepsis patient data for model training and test. Following feature selection, various machine learning algorithms were employed, including Decision Tree (DT), Efficient Neural Network (ENet), k-Nearest Neighbor (KNN), Light Gradient Boosting Machine (LightGBM), Multi-Layer Perceptron (MLP), Multinomial Mixture Model (Multinom), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost). A five-fold cross-test strategy was implemented to minimize bias and assess model performance. SHapley Additive exPlanations (SHAP) was used to interpret the results.
ResultsA total of 6,866 critically ill sepsis patients were analyzed, of whom 5,896 developed AKI during hospitalization The RF model demonstrated superior performance, attaining an average AUC score of 0.89 on the ROC curve. SHAP analysis provided detailed insights into feature importance, including urine output, BMI, SOFA score, and maximum blood urea nitrogen, enhancing the clinical applicability of the model.
ConclusionThe machine learning models developed in this study effectively predicted the stages of AKI in severely ill sepsis patients, with the Random Forest model demonstrating optimal performance. SHAP analysis offered crucial insights into the risk factors, facilitating timely and personalized interventions within a clinical setting. Additional multi-center research is essential to confirm the validity of these findings and to ultimately improve patient outcomes and quality of life.