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在治疗选择偏差下学习动态二进制治疗策略:一种保守的Q学习方法,具有代表性平衡

Animesh Kumar Paul¹, Russell Greiner²

¹Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada.

Journal of biomedical informatics

|February 12, 2026

概括

本研究介绍了CQL-RB,这是一种从偏差数据中学习安全动态处理方案 (DTR) 的新方法. CQL-RB 改善了患者代表性对齐,从而在医疗保健模拟中实现了更有效,更安全的 DTR.

科学领域:

人工智能的人工智能
机器学习机器学习
医疗保健信息学医疗保健信息学

背景情况:

从观测数据中学习动态治疗方案 (DTR) 具有挑战性,因为治疗选择偏差.
线下强化学习 (RL) 提供了一个有希望的方法,但需要仔细处理有偏见的数据,以确保安全性和稳定性.

研究的目的:

开发一种强大的线下强化学习 (RL) 方法,从观察性医疗数据中学习安全的动态治疗方案 (DTR),这些数据显示治疗选择偏差.
通过一种代表性平衡机制来增强保守的Q学习 (CQL),以减轻分配转移和提高政策安全性.

主要方法:

拟议的CQL-RB,通过基于整数概率度量 (IPM) 的表示平衡惩罚 (MMD或能量距离) 来增强保守的Q-学习 (CQL).
在两个现实的模拟器 (EpiCare,AhnChemo) 上评估了CQL-RB,并采用临床医生的行为政策来模拟选择偏差.
将CQL-RB与多个基线进行比较,通过预期回报和不良事件计数评估结果,使用政策之外的评估.

主要成果:

在EpiCare和AhnChemo模拟器中,CQL-RB的表现始终优于基线,实现了更高的预期回报率和更低或可比的不良事件率.
废弃性研究证实了表示平衡项的贡献,显示当删除时性能降低.
该方法证明了对中等惩罚权重的稳定性,仅在过大值时才会出现性能恶化.

结论:

在存在治疗选择偏差的情况下,代表性平衡显著提高了DTR学习的保守离线RL.
CQL-RB成功地通过调整患者表示来学习更有效和更安全的DTR,同时保持CQL的安全性质.
解决治疗选择偏见对于制定医疗保健中强大和安全的动态治疗政策至关重要.

关键词:

保守的Q学习.动态处理方案动态处理方案医疗保健的决策决策.在线非线增强学习.代表性的平衡是代表性的平衡.治疗选择偏差治疗选择偏差

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