LightGBM outperforms other machine learning techniques in predicting graft failure after liver transplantation: Creation of a predictive model through large-scale analysis-论文-万方医学网

首页> > LightGBM outperforms other machine learning techniques in predicting graft failure after liver transplantation: Creation of a predictive model through large-scale analysis

LightGBM outperforms other machine learning techniques in predicting graft failure after liver transplantation: Creation of a predictive model through large-scale analysis

导出原文传递

翻译打印收藏纠错

第一作者： Rintaro Yanagawa

作者： Rintaro Yanagawa ^[1] ; Kazuhiro Iwadoh ; Miho Akabane ; Yuki Imaoka ; Kliment Krassimirov Bozhilov ; Marc L Melcher ; Kazunari Sasaki

作者单位： Graduate School of Medicine, Kyoto University, Kyoto, Japan. ^[1]

关键词 LightGBM XGBoost gradient boosting decision tree graft failure liver transplantation machine learning rolling cross‐validation.

发布时间 2024-04-21

翻译满意度评价：

准确一般字义偏差语意偏离译文作用小提交

浏览0

相似文献

中文期刊
外文期刊
学位论文
会议论文

参考文献：
指定格式：	NoteExpress EndNote RefWorks NoteFirst

法律状态公告日	法律状态	法律状态信息

检索历史清除

LightGBM outperforms other machine learning techniques in predicting graft failure after liver transplantation: Creation of a predictive model through large-scale analysis

相似文献

同项目论文

相关期刊

检索历史 清除

LightGBM outperforms other machine learning techniques in predicting graft failure after liver transplantation: Creation of a predictive model through large-scale analysis

相似文献

同项目论文

相关期刊

检索历史清除