对动态治疗方案的异质Q学习采用形对对融合方法
Jubo Sun1,2, Wensheng Zhu1, Guozhe Sun3
1Key Laboratory for Applied Statistics of MOE and School of Mathematics and Statistics, Northeast Normal University, Changchun, China.
Statistics in medicine
|February 5, 2026
概括
异质Q学习通过考虑患者子组来改善动态治疗方案. 与标准方法相比,这种方法可以提高最佳治疗策略的估计,正如模拟和现实世界高血压数据所示.
科学领域:
- 生物统计学 生物统计学
- 机器学习 机器学习
- 医疗信息学 医疗信息学
背景情况:
- 动态治疗方案优化了顺序决策,以获得最大的患者益处.
- 现有的方法通常假定人口均性,可能限制有效性.
- 潜在的患者异质性可以显著影响治疗结果的估计.
研究的目的:
- 开发一种用于估计最佳动态治疗方案的新方法,以考虑患者异质性.
- 采用形双向融合惩罚方法引入异质的Q学习.
- 评估与标准的Q-learning相对应的拟议方法.
主要方法:
- 拟议的异质Q学习算法采用形双向融合惩罚方法.
- 采用了乘数交替方向方法 (ADMM) 来解决处罚最小平方问题.
- 通过模拟研究和来自中国农村高血压控制项目 (CRHCP) 的现实数据集进行验证.
主要成果:
- 与标准的Q学习方法相比,异构的Q学习表现出了更好的表现.
- 该方法有效地估计了在没有观察到的患者异质性的情况下,最佳的动态治疗方案.
- 对CRHCP数据集的成功应用突出显示了其实用性.
结论:
- 异质Q学习通过解决人口异质性,为个性化医疗提供了一个强大的框架.
- 提出的方法提高了动态治疗方案估计的准确性和可靠性.
- 这种方法具有显著的潜力,可以在复杂的健康场景中改善临床决策.
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