整合决策建模和机器学习以告知治疗分层
David Glynn1, John Giardina2, Julia Hatamyar1
1Centre for Health Economics, University of York, York, UK.
Health economics
|April 26, 2024
概括
使用机器学习 (ML) 和决策建模来分层处理治疗决策可以改善健康结果. 将ML集成到决策模型中,并使用政策树来定义患者子组,可以增加增量净健康益处 (INHB).
科学领域:
- 决策分析 决策分析
- 卫生经济学 卫生经济学
- 医疗保健中的机器学习
背景情况:
- 传统的"一刀切" (OSFA) 治疗方法正在被分层决策所取代.
- 了解患者对治疗有效性和成本效益的共同变量影响对于分层至关重要.
- 机器学习 (ML) 方法可以在没有预先指定子组的情况下识别结果异质性.
研究的目的:
- 提出一种方法,将ML估计与决策建模相结合,以获得长期的,与政策相关的结果.
- 开发一种新的政策树实施方法,以根据决策模型的结果来定义子组.
- 评估ML整合和子组分层对治疗决策的影响.
主要方法:
- 将基于ML的生存时间估计集成到决策建模框架中.
- 实施政策树算法,以使用决策模型输出来定义患者子组.
- 应用到缩血压干预试验 (SPRINT) 数据,比较标准与强化血压目标.
主要成果:
- 将ML整合到决策模型中可以改变OSFA政策的估计增量净健康益 (INHB).
- 通过基于树的算法识别的ML定义的子组来分层处理决策,可以增强INHB估计.
- 斯普林特试验数据表明,个性化治疗策略比OSFA有潜在的好处.
结论:
- 将ML与决策建模相结合,为个性化治疗策略提供了一种强大的方法.
- 通过ML驱动的政策树来识别子组可以优化治疗决策并改善健康经济结果.
- 这种综合方法支持向更有效和更具成本效益的分层医疗保健迈进.
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