Predicting biliary complications after liver transplantation: a machine learning and nomogram-based exploratory study

Abstract Background Accurate risk stratification of biliary complications (BCs) after liver transplantation (LT) remains challenging. This study aimed to develop and validate a machine learning (ML) and nomogram framework comprising a ML-based web calculator and a clinically interpretable nomogram for post-LT BCs. Methods This retrospective study analyzed 133 LT patients (2011–2025), randomly split into training ( n = 94) and validation ( n = 39) sets. Predictors were identified using Least Abso