An Interpretable Prediction Model for Acute Kidney Injury Based on XGBoost and SHAP

The development of Acute Kidney Injury (AKI) during admission to the Intensive Care Unit (ICU) is associated with increased morbidity and mortality. The objective of this study is to develop a machine learning-based framework for interpretable AKI prediction in critical care that can achieve both good prediction and interpretation capability. Data extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) include patient age, gender, vital signs and lab values during the first day of ICU admission and subsequent hospitalization. In this study, the prediction performance of the XGBoost model is demonstrated by comparing it to four other machine learning models. In addition, the SHapley Additive exPlanation (SHAP) framework is used to provide individualized evaluation and explanations to enable personalized clinical decision support. The results show that XGBoost can predict AKI robustly with an Accuracy and the area Under the receiver operating Characteristic curve (AUC) of 0.824 and 0.840, respectively, which are higher than previous prediction models. Furthermore, a feature dependency analysis is conducted for two pairs of features and found decrease in urine volume and elevation of blood urea nitrogen indicates an increase of AKI risk. To sum up, this interpretable predictive model may help clinicians more accurately identify patients at risk of AKI in intensive care and provide better treatment for patients. In addition, the use of this interpretability framework increases model transparency and facilitates clinicians to analyze the reliability of predictive models.