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基于树的多目标强化学习用于估计耐受性的动态治疗方案

Yao Song¹, Lu Wang¹

¹Department of Biostatistics, University of Michigan, Ann Arbor, MI 48105, United States.

|February 16, 2024

概括

这项研究引入了个性化医疗的"宽容方案"概念,在优先事项冲突时提供多种可行的治疗规则. 新的多目标基于树的强化学习 (MOT-RL) 方法有效地估计了这些耐受性动态治疗方案 (tDTR).

科学领域:

生物统计学生物统计学
机器学习机器学习
个性化医疗是个性化的医疗.

背景情况:

动态治疗方案 (DTRs) 根据患者病史来个性化医疗决策.
现实世界的场景往往涉及多个相互竞争的目标,需要在治疗决策中进行权衡.
现有的方法可能无法充分解决多种最佳或近最佳治疗策略的情况.

研究的目的:

引入"宽容制度" (tDTR) 的概念,以适应在特定宽容率下的多个可行的个性化决策规则.
开发一种新的多目标基于树的强化学习 (MOT-RL) 方法,用于在多阶段,多处理环境中估计tDTR.
通过考虑多个目标和权衡来优化临床决策支持系统.

主要方法:

开发了一个基于树的多目标强化学习 (MOT-RL) 算法.
在每个阶段使用无监督决策树,通过半参数回归建模反事实结果.
使用标化增强反向概率加权估计器 (SAIPWE) 来构建优化纯度度度.
以向后感应的方式实现了算法,用于多个阶段的决策.

主要成果:

该MOT-RL方法直接估计最佳的DTR和tDTR,以适应决策者的偏好.
这种方法是稳固的,高效的,可解释的,在各种环境中灵活的.
成功应用MOT-RL来评估晚期前列腺癌的2阶段化疗方案.

结论:

拟议的"耐受性方案"概念和MOT-RL方法为具有竞争目标的个性化医疗提供了灵活的框架.
这种方法通过提供一套可行的,个性化的治疗策略来增强临床决策支持.
证明了MOT-RL在优化癌症治疗策略以改善患者的治疗结果方面的实际实用性.

关键词:

有关因果推理的推理.决策树是一个决策树.动态处理方案动态处理方案多目标优化优化个性化医疗是个性化的医疗.

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