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预测和诊断机器学习建模研究的综合报告准则:开发和验证

William Klement^1,2, Khaled El Emam^1,2

¹University of Ottawa, Ottawa, ON, Canada.

Journal of medical Internet research

|August 31, 2023

概括

机器学习 (ML) 研究报告不足,阻碍了理解和复制. 这项研究将ML报告准则整合到一个全面的检查清单中,以提高预后和诊断研究的质量和可重复性.

科学领域:

医疗信息学医疗信息学
人工智能的人工智能
生物统计学生物统计学

背景情况:

机器学习 (ML) 预后和诊断研究的报告往往不足,阻碍了理解和可重现性.
现有的指导方针涉及ML生命周期的特定方面,但缺乏全面的单独覆盖.

研究的目的:

整合现有的ML报告准则和检查清单.
创建一套全面的报告项目,用于在in-silico和影子模式的预后和诊断ML研究.

主要方法:

进行了文献搜索,确定了关于ML报告指南的192篇文章.
选和评估了17个高质量的源文件,用于报告项目.
整合,通过专家审查进行精细化,并将验证的报告项目纳入检查清单.

主要成果:

在5个类别中确定了37个报告项目:研究细节,数据,建模,评估和可解释性.
没有单一的来源文章涵盖了所有项目;可解释性和方法最少覆盖.
开发了一个检查清单,以促进更完整的报告.

结论:

现有的指导方针单独是不完整的,需要采取综合方法.
开发的检查清单旨在提高ML建模研究的质量和可重复性.

关键词:

诊断诊断诊断的诊断诊断诊断诊断的诊断机器学习是机器学习.模型评价模型评价模型培训培训模型培训预测模型预测模型预后预测预测的预测.预测模型的预测模型.报告准则报告准则可复制性指导方针可复制性指导方针

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