Transfer learning prediction of 2-year native liver survival in biliary atresia following Kasai portoenterostomy using early perioperative biochemical data up to 3 months postoperatively.

The five traditional machine learning models showed varying performance, with RF demonstrating the highest average AUC in the training set (AUC = 0.795, 95% CI 0.709-0.881).

GGT_3M, ALB_3M, DBIL_3M, DBIL_1M and GLO_1W were identified as the most critical predictive features.

A transfer learning-based clinical decision support system was deployed, providing dynamic visualization of individualized 24-month native liver survival trajectories.