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从机器学习模型的输出分数中估计赔率比:可能性和局限性
Ronit Nirel1, Naor Bauman2, Efrat Morin3
1Department of Statistics and Data Science, The Hebrew University of Jerusalem, Mt. Scopus, 9190501, Jerusalem, Israel. nirelr@mail.huji.ac.il.
Scientific reports
|February 16, 2026
概括
机器学习 (ML) 模型现在可以估计暴露-反应关联,就像在流行病学中的几率比率 (ORs). 将ML与后勤回归 (LR) 集成的混合估计器为复杂的健康数据提供可解释的结果.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 机器学习 机器学习
背景情况:
- 估计暴露-反应关联在流行病学中至关重要.
- 机器学习 (ML) 提供了先进的建模,但缺乏直接的流行病学解释性 (例如,赔率比 - ORs).
研究的目的:
- 开发和评估使用ML输出的ORs的混合估计器.
- 弥合 ML 的预测能力和流行病学对可解释的关联估计的需求之间的差距.
主要方法:
- 提出了八个混合OR估计器,将ML分类器输出与后勤回归 (LR) 调整因子结合起来.
- 引入了基于部分依赖函数的两个估计器.
- 应用了对LR,随机森林 (RF) 和梯度增强 (GB) 模型的估计器,使用与温度相关的健康数据.
主要成果:
- 梯度增强 (GB) 模型产生了与LR (87%在95%CI范围内) 大致一致的估计.
- 随机森林 (RF) 的性能在数据集之间有显著差异 (0-60%在95%CI内).
- 基于GB的置信区间 (CI) 比LRCI窄13-59%.
结论:
- 混合估计器通过提供可解释的关联指标来增强ML在流行病学中的整合.
- 渐变增强在流行病学研究中显示出可靠的暴露-反应估计的前景.
- 进一步的研究可以利用ML进行强有力的流行病学推断.
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