Application of machine learning algorithms to recipient-related data for the prediction of short-term survival following liver transplantation

Background and Objective: Current prediction of short-term survival after liver transplantation (LT) primarily relies on linear clinical scores such as the MELD, Donor-MELD, or Balance-of-Risk score. However, these model scores often provide limited predictive accuracy and depend on donor-related parameters that are only available shortly before transplantation, limiting their use for early risk stratification on the waiting list. The aim of this study was to develop and evaluate a recipient-based machine learning (ML) model to predict short-term post-transplant survival, using only variables available before organ allocation.