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半监督的政策之外的强化学习和价值估计,用于动态处理制度

Aaron Sonabend-W¹, Nilanjana Laha², Ashwin N Ananthakrishnan³

¹Department of Biostatistics, Harvard University, Boston, USA.

Journal of machine learning research : JMLR

|January 29, 2026

概括

本研究引入了一种半监督学习 (SSL) 方法,以改善动态治疗方案的强化学习 (RL). 它有效地使用有限的标记数据和大量的未标记数据,从临床笔记中估计患者的结果.

科学领域:

* 计算统计和机器学习应用于医疗保健.
* 开发用于个性化医疗的先进算法.

背景情况:

*强化学习 (RL) 显示出动态治疗方案的潜力,但依赖于准确的健康结果数据.
* 临床注释通常包含结果信息,但手动提取资源密集,导致小标记数据集.
* 现有的方法在有限的标记数据上扎,以训练有效的RL模型.

研究的目的:

* 开发一种半监督学习 (SSL) 方法,用于动态治疗方案中的强化学习 (RL).
* 为了利用具有观察结果的小标记数据集和具有结果替代品的大型未标记数据集.
* 解决将SSL泛化为动态处理方案的挑战,包括未知特征分布和非信息替代变量.

主要方法:

* 提出了一种半监督的,高效的Q学习方法,以及两倍强大的政策外价值估计.
* 开发了一个修改后的SSL框架,以处理预测结果的替代品,而不是政策信息.
* 提供了Q函数和值函数估计器的理论分析,以量化SSL效率的提高.

主要成果:

* 拟议的SSL方法与纯监督方法相比,至少具有相当的效率.
* 该方法对由错误指定的归算模型产生的潜在偏差具有稳定性.
* 理论结果量化了通过SSL将未标记的数据纳入实现的效率增长.

结论:

*SSL提供了一种有效的策略,用于使用有限的标记临床数据来增强动态治疗方案的RL.
* 拟议的方法在具有挑战性的医疗保健数据场景中提供了高效和可靠的价值估计.
* 这项工作通过改进数据利用来推进机器学习在个性化医学中的应用.

关键词:

这就是Q-learning.价值函数具有两倍强大的功能.动态处理制度动态处理制度在政策之外的学习.强化学习是一种强化学习.半监督学习半监督学习

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