Explainable machine learning for prognostic modeling of waitlist mortality in cirrhotic liver transplantation.

ABSTRACT: Accurate mortality prediction in liver transplant (LT) candidates is essential for improving organ allocation and prioritization. Models like Model for End-Stage Liver Disease (MELD) are widely used, but may overlook complex nonlinear interactions between risk factors. Machine learning (ML) offers improved predictive accuracy but often at the expense of interpretability. In this study, we conduct a comprehensive comparison of three MELD-based scores against advanced ML models, including LDA, TabNet, RF and LightGBM to predict 3-, 6-, and 12-month waitlist mortality, using retrospective data from the UNOS/OPTN registry. SHapley Additive exPlanations (SHAP) were exploited to provide deeper insights into the best model's decision-making process, offering both global and local explanations whi

SUBMITTER: Halimi A 

PROVIDER: S-EPMC12743420 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature