AI 中文总结
针对现有结局加权学习类方法无法量化个体治疗决策不确定性的问题，本文将贝叶斯OWL扩展至多阶段场景，提出反向贝叶斯结局加权学习（BBOWL），通过模拟研究验证了其性能。
AI 中文摘要
精准医学的核心目标是从数据中学习最优动态治疗方案（DTRs）。基于分类的方法，如针对单阶段问题的结局加权学习（OWL）和针对多阶段问题的反向结局加权学习（BOWL），利用机器学习直接学习最优DTRs。然而，这些方法缺乏自然的方式来量化个体层面治疗决策的不确定性。在本文中，我们将OWL的贝叶斯重构方法——贝叶斯OWL扩展到多阶段场景，将该方法命名为反向贝叶斯结局加权学习（BBOWL）。与BOWL类似，我们的方法通过反向归纳直接学习最优DTRs；与现有方法不同，我们的方法在DTR学习过程中向后传播不确定性，并为个体化治疗推荐提供不确定性量化。我们对BBOWL进行了理论论证，并通过模拟研究验证了其性能。
英文摘要
A central objective of precision medicine is learning optimal dynamic treatment regimes (DTRs) from data. Classification-based methods, like outcome weighted learning (OWL) for single-stage and backward OWL (BOWL) for multi-stage problems, leverage machine learning to directly learn optimal DTRs. However, these methods lack a natural way to quantify uncertainty in treatment decisions at the individual level. In this paper, we extend Bayesian OWL, a Bayesian reformulation of OWL, to the multi-stage setting. We call this method backward Bayesian outcome weighted learning (BBOWL). Like BOWL, our method directly learns an optimal DTR via backward induction, and unlike existing methods, our approach propagates uncertainty backward through the DTR learning process and provides uncertainty quantification of individualized treatment recommendations. We present a theoretical justification of BBOWL and verify its performance via a simulation study.
Comments9 pages, 3 figures, 1 table
