医学中随机森林变量重要性指标的使用和滥用:通过事件中风预测的演示
Meredith L Wallace1,2, Lucas Mentch3, Bradley J Wheeler4
1Department of Psychiatry, University of Pittsburgh, 3811 O'Hara Street, Pittsburgh, PA, 15231, USA. lotzmj@upmc.edu.
BMC medical research methodology
|June 19, 2023
概括
研究人员经常在随机森林模型中使用有偏见的"自制"变量重要性指标 (VIMPs). 现代的"淘汰VIMP"为分析复杂的医疗数据提供了更易于解释和更准确的替代方案.
科学领域:
- 医疗信息学医学信息学
- 统计建模 统计建模
- 机器学习是机器学习.
背景情况:
- 随机森林对于分析复杂的医疗数据来说非常强大.
- 目前的"包外" (OOB) 变量重要性指标 (VIMPs) 有统计上的局限性,包括偏差和糟糕的解释性.
- 一个现代的替代方案,即"淘汰VIMPs",为理解模型预测提供了优势.
研究的目的:
- 评估当前OOB VIMP在医学研究中的局限性.
- 引入和推使用可解释的"敲门VIMP"的有组织策略.
- 在预测事故中风时,比较OOB和伪造VIMP.
主要方法:
- 对最近50个随机森林手稿的文献综述,以评估VIMP使用情况.
- 开发一种随机森林模型,利用睡眠心脏健康研究数据预测5年内发生的中风.
- 使用OOB VIMP与仿制VIMP获得的结果的比较.
主要成果:
- 几乎所有审查的论文都在VIMP应用中表现出重大局限性.
- OOB VIMPs确定了相关的肺功能变量作为关键中风预测因素.
- 伪造的VIMP揭示了药物,医疗风险因素,年龄,血压和吸烟作为重要的预测因素,导致了不同的结论.
结论:
- 过度依赖OOB VIMP可能会产生误导性的研究结果.
- 实现可解释和无偏见的"淘汰VIMP"对于推进医学机器学习应用至关重要.
- 假冒VIMP的广泛采用将引导研究人员进行更有意义的发现.
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