利用机器学习来预测千年队列研究中参与者对随访健康调查的反应
Wisam Barkho1,2, Nathan C Carnes3, Claire A Kolaja3,4
1Deployment Health Research Department, Naval Health Research Center, San Diego, CA, USA. wisam.z.barkho.ctr@health.mil.
Scientific reports
|October 29, 2024
概括
在纵向研究中,预测参与者不反应至关重要. 机器学习,特别是使用历史数据,有效地提高了千年队列研究的调查响应率.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 医疗保健服务研究 医疗服务研究
背景情况:
- 长度研究,如千年队列研究依赖调查数据来评估军事服务的影响.
- 参与者对后续调查的不响应可能会损害研究的有效性和通用性.
- 预测分析提供了一种有前途的方法来识别非响应预测因素.
研究的目的:
- 开发一种高技能机器学习分类器,用于预测千年队列研究中参与者的非响应.
- 评估通过隐性类分析 (LCA) 分析的历史调查响应数据对预测性能的影响.
- 为了确定调查非响应的关键预测因素.
主要方法:
- 应用了六个监督机器学习算法来预测对2021年后续调查的反应.
- 隐性类分析 (LCA) 用于根据历史调查响应模式对参与者进行分类.
- 预测模型与不包括LCA变量进行了比较,随后进行了特征分析.
主要成果:
- 包括LCA变量导致所有六个算法的性能相似.
- 没有LCA变量,随机森林的表现优于基准回归模型,但整体预测准确性下降.
- 功能分析确定LCA变量是调查响应最重要的预测因素.
结论:
- 历史调查响应模式对于改善纵向研究中参与者不响应的预测至关重要.
- 机器学习算法,特别是当有历史数据可用时,可以提高预测的准确性.
- 实施这些预测方法可以优化外展策略,提高调查响应率,并减轻非响应偏差.
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