Comparison of binary classifier performance among four machine learning models for early-loss, intermediate-loss, and late/no-loss groups.

<p>Comparison of binary classifier performance among four machine learning models for early-loss, intermediate-loss, and late/no-loss groups.</p>

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Bibliographische Detailangaben
1. Verfasser:	Raiki Yoshimura (22676573) (author)
Weitere Verfasser:	Naotoshi Nakamura (12083048) (author), Takeru Matsuura (22676576) (author), Takeo Toshima (13988776) (author), Takasuke Fukuhara (673475) (author), Kazuyuki Aihara (44090) (author), Katsuhito Fujiu (559488) (author), Shingo Iwami (266092) (author), Tomoharu Yoshizumi (2621434) (author)
Veröffentlicht:	2025
Schlagworte:	Medicine urgent need exists stage liver disease shorter waiting times recent years thanks populations using data model enabled us highly heterogeneous sample appropriate patient care 30 days postoperatively distinct population similar different survival times hierarchical prediction method better graft quality survival time loss population graft loss better performance unexpected infections transplanted organ three groups several models next categorized mediated rejection loss groups loss group living donors gained importance five groups conventional models
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