Explainable machine learning for the assessment of donor grafts in liver transplantation

The shortage of liver grafts compared to recipients necessitates precise organ assessment. This study aimed to develop a Machine learning (ML) model to predict postoperative delayed graft function (DGF) and visualize the decision-making process for clinical application. Data from 5242 donor-recipien...

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Veröffentlicht in:	Hepatology research Jg. 55; H. 6; S. 908 - 921
Hauptverfasser:	Zhixing, Liang, Linsen, Ye, Peng, Jiang, Siyi, Dong, Haoyuan, Yu, Kun, Li, Siqi, Li, Yongwei, Hu, Mingshen, Zhang, Wei, Liu, Hua, Li, Shuhong, Yi, Guihua, Chen, Xiao, Xu, Shusen, Zheng, Yang, Yang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Netherlands Wiley Subscription Services, Inc 01.06.2025
Schlagworte:	Accuracy Algorithms Blood levels Decision making Learning algorithms Liver transplantation Liver transplants Machine learning Survival γ-Glutamyltransferase eXtreme gradient boosting algorithms machine learning SHapley additive exPlaination delayed graft function liver transplantation
ISSN:	1386-6346, 1872-034X
Online-Zugang:	Volltext
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