LightGBM outperforms other machine learning techniques in predicting graft failure after liver transplantation: Creation of a predictive model through large‐scale analysis
Rintaro Yanagawa,
Kazuhiro Iwadoh,
Miho Akabane
et al.
Abstract:BackgroundThe incidence of graft failure following liver transplantation (LTx) is consistent. While traditional risk scores for LTx have limited accuracy, the potential of machine learning (ML) in this area remains uncertain, despite its promise in other transplant domains. This study aims to determine ML's predictive limitations in LTx by replicating methods used in previous heart transplant research.MethodsThis study utilized the UNOS STAR database, selecting 64,384 adult patients who underwent LTx between 2… Show more
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