如何阅读使用机器学习的文章:医疗文献的用户指南
Yun Liu1, Po-Hsuan Cameron Chen1, Jonathan Krause1
1Google Health, Palo Alto, California.
JAMA
|November 13, 2019
概括
使用机器学习 (ML) 的新临床诊断工具需要严格的评估. 这包括推导,验证和确定临床有效性,以及评估ML模型的适当性和可靠结果的参考标准严格性.
科学领域:
- 医疗信息学
- 在医疗保健中的机器学习
- 临床诊断
背景情况:
- 最近出现了许多基于机器学习 (ML) 的临床诊断工具.
- 这些工具需要一个结构化的评估过程,不管它们的衍生方法.
研究的目的:
- 概述基于ML的临床诊断工具的基本评估框架.
- 强调获得,验证和确定临床有效性的关键步骤.
主要方法:
- 评估选择的ML模型对输入数据类型和大小的适用性.
- 在独立于验证集的数据集上调整模型的超参数.
- 根据验证组的定义参考标准对ML模型的结果进行评估.
主要成果:
- 这项研究强调了三步评估的必要性:导出,验证和临床有效性.
- 它强调了评估ML模型类型,数据适用性和超参数调整的重要性.
- 参考标准的严格性,可能与黄金标准相比,对于验证至关重要.
结论:
- 所有临床诊断工具,尤其是使用ML的诊断工具,都需要一个系统的三步评估过程.
- 适当评估ML模型参数和参考标准可确保诊断工具的可靠性和临床实用性.
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