机器学习方法用于估计个性化治疗效果,用于卫生技术评估
Yingying Zhang1, Noemi Kreif1,2, Vijay S Gc3
1Centre for Health Economics, University of York, UK.
概括
机器学习 (ML) 方法显示出从观察数据中估计个性化治疗效应 (ITE) 的前景. 然而,当前的ML算法在处理复杂的健康数据挑战方面存在局限性,需要进一步开发以进行可靠的健康技术评估.
科学领域:
- 因果推断和机器学习 (ML) 在健康经济学和结果研究中的应用.
- 开发和评估用于医疗技术评估的统计和计算方法.
背景情况:
- 个性化治疗效应 (ITE) 估计患者特异性治疗的有效性,帮助个性化健康政策.
- 机器学习 (ML) 提供了ITE估计的先进方法,但它们适用于医疗技术评估 (HTA) 的适用性尚未得到充分理解.
研究的目的:
- 对估计ITE的ML方法进行范围审查.
- 评估这些ML方法对于卫生技术评估 (HTA) 应用的适当性.
- 在HTA中识别当前ML方法的挑战和局限性.
主要方法:
- 系统评估用于ITE估计的ML算法.
- 基于HTA挑战的ML方法的分类:时间变化的混,时间到事件数据和不确定性量化.
- 范围审查方法,以识别和综合相关文献.
主要成果:
- 对于具有基线混和二进制/连续结果的更简单场景,存在广泛的ML算法.
- 很少有ML算法有效处理时间变化或未观察到的混.
- 目前没有ML算法可以估计ITEs的时间到事件结果与时间变化的混;许多缺乏正式的不确定性量化.
结论:
- 目前用于ITE估计的ML方法对于HTA是不够的,原因是由于观察数据复杂性的限制.
- 关键的挑战包括处理时间到事件结果,时间变化/隐藏混,以及量化不确定性.
- 进一步开发ML方法对于其在成本效益分析和HTA中的可靠应用至关重要.
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