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随机森林用于反复事件的动态风险预测:一种伪观测方法
Abigail Loe1, Susan Murray1, Zhenke Wu1,2
1Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109, United States.
Biostatistics (Oxford, England)
|March 14, 2025
概括
这项研究引入了一个随机森林模型来预测反复发生的事件,通过有效处理复杂的数据和患者病史来优于传统方法. 这一进步改善了各种领域的动态风险预测.
科学领域:
- 生物统计学 生物统计学
- 医疗保健中的机器学习
- 生存分析的分析.
背景情况:
- 在临床和社会研究中,复发性事件很普遍,但动态风险预测方法有限.
- 分析审查的反复事件数据对传统的回归模型构成挑战,尤其是复杂的相互作用和众多预测因素.
- 机器学习,特别是随机森林回归,为处理具有非线性和多路交互的高维数据提供了有前途的方法.
研究的目的:
- 开发一种随机森林方法,以使用重建的经过审查的纵向数据动态预测剩余的无事件概率.
- 适应现有的XMT回归框架来预测这些概率,承认其固有的局限性.
- 将患者特异性的反复事件历史纳入预测模型,即使这些数据不完整.
主要方法:
- 开发了一种随机森林算法,用于在定义的后续窗口 (τ) 上预测无事件生存概率.
- 现有的XMT (eXponentially Modified Treatment) 回归框架被修改为预测类似的概率.
- 两种方法都被评估,以考虑患者病史的能力,特别是当事件时间在受试者内相关时.
主要成果:
- 与修改后的XMT方法相比,随机森林算法在预测无事件概率方面表现优异,特别是在复杂的预测器-结果关联的情况下.
- 该研究强调了将过去的反复事件历史纳入研究的关键重要性,以便在事件时间与个人之间相关时准确预测.
- 拟议的随机森林方法使用真实世界数据验证了慢性阻塞性肺部疾病患者的复发性恶化.
结论:
- 开发的随机森林算法为反复事件的动态风险预测提供了增强的能力,特别是在复杂的场景中.
- 整合患者病史显著提高了相关的复发事件的预测准确性.
- 这种方法提供了一种有价值的工具,可以在各种研究领域推进对被审查的反复事件数据的分析.
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