Interpretable multitemporal liver function indicator model for prediction and risk factor analysis of drug induced liver injury.

The occurrence of liver injury during cancer treatment is extremely harmful. The risk factors for drug.induced liver injury (DILI) in the pancreatic cancer population have not been investigated. This study aims to develop and validate an interpretable decision tree (DT) model for the early prediction of DILI in pancreatic cancer patients using multitemporal clinical data and screening for related risk factors. A retrospective collection of data was conducted on 307 patients, the training set (n = 215) was used to develop the model, and the test set (n = 92) was used to evaluate the model. The classification and regression trees algorithm was employed to establish the DT model. The Shapley Additive explanations (SHAP) method was used to facilitate clinical interpretation. Model performance was assessed using AUC and the Hosmer‒Lemeshow test. The DT model exhibited superior diagnostic efficacy, the AUC values were 0.995 and 0.994 in the training and test sets, respectively. Four risk factors associated with DILI occurrence were identified: delta.albumin, delta.ALT, and post (AST: ALT), and post.GGT. The multiperiod liver function indicator.based interpretable DT model predicted DILI occurrence in the pancreatic cancer population and contributes to personalized clinical management of pancreatic cancer patients.