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相关实验视频

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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在开发使用机器学习在瘤学中的临床预测模型时,需要更大的样本大小:方法论系统性审查.

Biruk Tsegaye1, Kym I E Snell2, Lucinda Archer2

  • 1Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK.

Journal of clinical epidemiology
|January 15, 2025
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概括

使用机器学习 (ML) 的瘤学临床预测模型往往缺乏足够的样本大小. 大多数研究没有证明样本大小是合理的,低于回归模型所需的最小值,这表明ML模型的缺陷可能更大.

关键词:
机器学习 机器学习方法论 方法论 方法论在瘤学瘤学.预测模型的预测模型.样本的大小 样本大小系统性审查 系统性审查

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科学领域:

  • 在瘤学瘤学.
  • 生物统计学 生物统计学
  • 机器学习 机器学习

背景情况:

  • 足够的样本大小对于开发强大的临床预测模型至关重要.
  • 机器学习 (ML) 模型越来越多地用于瘤学中的二元结果预测.
  • 对于基于回归的模型,所需的最小样本大小 (Nmin) 作为基准.

研究的目的:

  • 在使用ML的瘤预测模型研究中审查样本大小的合理性.
  • 为了将ML模型开发中使用的样本大小与回归模型所需的最小样本大小 (Nmin) 进行比较.

主要方法:

  • 我们对Medline数据库进行了系统的搜索,搜索了2022年12月发表的基于ML的瘤学预测模型.
  • 对包含的研究,审查了样本大小的理由.
  • 计算了所需的最小样本大小 (Nmin),并与研究中使用的样本大小进行了比较.

主要成果:

  • 在36项研究中,只有1项研究证明了其样本大小.
  • 对于17项 (47%) 的研究,可以计算Nmin;只有5项达到这一最低值.
  • 观察到事件中302名参与者的中位数赤字;ML模型可能需要比回归模型更大的样本大小.

结论:

  • 在瘤学中开发ML预测模型的研究很少证明样本大小.
  • 样本大小经常低于Nmin,这可能导致过度拟合和不准确的风险估计.
  • 研究人员应报告并满足最低样本大小要求,特别是考虑到ML模型的更高要求.