{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T02:01:17Z","timestamp":1788832877753,"version":"build-2803163510"},"reference-count":34,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T00:00:00Z","timestamp":1712880000000},"content-version":"vor","delay-in-days":11,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Clinical Transplantation"],"published-print":{"date-parts":[[2024,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study utilized the UNOS STAR database, selecting 64,384 adult patients who underwent LTx between 2010 and 2020. Gradient boosting models (XGBoost and LightGBM) were used to predict 14, 30, and 90\u2010day graft failure compared to conventional logistic regression model. Models were evaluated using both shuffled and rolling cross\u2010validation (CV) methodologies. Model performance was assessed using the AUC across validation iterations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In a study comparing predictive models for 14\u2010day, 30\u2010day and 90\u2010day graft survival, LightGBM consistently outperformed other models, achieving the highest AUC of.740,.722, and.700 in shuffled CV methods. However, in rolling CV the accuracy of the model declined across every ML algorithm. The analysis revealed influential factors for graft survival prediction across all models, including total bilirubin, medical condition, recipient age, and donor AST, among others. Several features like donor age and recipient diabetes history were important in two out of three models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>LightGBM enhances short\u2010term graft survival predictions post\u2010LTx. However, due to changing medical practices and selection criteria, continuous model evaluation is essential. Future studies should focus on temporal variations, clinical implications, and ensure model transparency for broader medical utility.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/ctr.15316","type":"journal-article","created":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T09:44:30Z","timestamp":1712915070000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["LightGBM outperforms other machine learning techniques in predicting graft failure after liver transplantation: Creation of a predictive model through large\u2010scale analysis"],"prefix":"10.1111","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-7930-5306","authenticated-orcid":false,"given":"Rintaro","family":"Yanagawa","sequence":"first","affiliation":[{"name":"Graduate School of Medicine Kyoto University  Kyoto 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