The promise and reality of machine-learning models in kidney transplantation

被引：0

作者：

Schold, Jesse D. ^{[1
,2
,3
]}

机构：

[1] Univ Colorado Anschutz Med Campus, Dept Surg, Aurora, CO USA

[2] Univ Colorado Anschutz Med Campus, Colorado Sch Publ Hlth, Dept Epidemiol, Aurora, CO USA

[3] Univ Colorado Anschutz Med Campus, Dept Surg, 1635 Aurora Court,Floor 7, Aurora, CO 80045 USA

来源：

KIDNEY INTERNATIONAL | 2023年 / 103卷 / 05期

关键词：

SURVIVAL BENEFIT; DIALYSIS; TIME;

D O I：

10.1016/j.kint.2023.02.008

中图分类号：

R5 [内科学]; R69 [泌尿科学（泌尿生殖系疾病）];

学科分类号：

1002 ; 100201 ;

摘要：

There have been numerous advances in statistical methods and computing technologies over the past decades, including the use machine-learning models. In the current study, Truchot et al. rigorously evaluated the performance of different machine-learning models compared with traditional Cox proportional hazard models. Results of the study indicated that a Cox model had equivalent superior performance than machine-learning models and can be on for predicting graft survival in kidney transplantation.

引用

页码：835 / 836

页数：2

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