Building Prediction Models of Cisplatin-Induced Acute kidney Injury and Analyzing Their Decision

Abstract

Although Cisplatin is an effective anti-cancer drug, it is known that there is a risk of Cisplatin-induced acute kidney injury (Cis-AKI). In this study, machine learning-based prediction models were built to detect early-stage Cis-AKI. Our prediction models are designed for the electric health records of individual patients outside the intensive care unit. In the experiments, some of our models achieved moderate predictive accuracy for Cis-AKI, and their predictions visualized by SHapley Additive exPlanations (SHAP) or their decision rules coincide with the medical insights on Cis-AKI.