PERSONALIZED PREDICTIVE VALUE OF ARTIFICIAL INTELLIGENCE FOR ACUTE KIDNEY INJURY AND MORTALITY RISK IN HOSPITALIZED PATIENTS RECEIVING FUROSEMIDE

Objective To analyze the association between the use of furosemide in hospitalized patients and the risk of acute kidney injury (AKI) and death based on machine learning and to construct a predictive model. Methods The study inclu-ded 18 998 hospitalized patients who had used furosemide in our hospital from October 2017 to October 2020. The predictive model for evaluating furosemide-associated AKI and mortality risks was established using eight machine learning algorithms including Light Gradient Boosting Machine (LightGBM). The receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to assess the discrimination, calibration, and clinical utility of the model. Feature importance analysis of the predictive model with the highest area under the curve (AUC) was conducted using SHAP summary plots, while SHAP force and decision plots were used to explain the individualized decision-making process for predicting AKI and mortality. Results Among the eight machine learning models, th