机器学习模型在肾移植中的前景与现实。

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

机构信息

Department of Surgery, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA; Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA.

出版信息

Kidney Int. 2023 May;103(5):835-836. doi: 10.1016/j.kint.2023.02.008.

DOI:10.1016/j.kint.2023.02.008

PMID:37085256

Abstract

There have been numerous advances in statistical methods and computing technologies over the past decades, including the use of 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 or superior performance than machine-learning models and can be relied on for predicting graft survival in kidney transplantation.

摘要

在过去的几十年中，统计方法和计算技术取得了众多进展，包括使用机器学习模型。在本研究中，Truchot 等人严格评估了与传统 Cox 比例风险模型相比，不同机器学习模型的性能。研究结果表明，Cox 模型的性能与机器学习模型相当或更优，可用于预测肾移植中的移植物存活率。