Dynamic treatment regimes (DTRs) are used in medicine to tailor sequential treatment decisions to patients by considering patient heterogeneity. Common methods for learning optimal DTRs, however, have shortcomings: they are typically based on outcome prediction and not treatment effect estimation, or they use linear models that are restrictive for patient data from modern electronic health records. To address these shortcomings, we develop two novel methods for learning optimal DTRs that effectively handle complex patient data. We call our methods DTR-CT and DTR-CF. Our methods are based on a data-driven estimation of heterogeneous treatment effects using causal tree methods, specifically causal trees and causal forests, that learn non-linear relationships, control for time-varying confounding, are doubly robust, and explainable. To the best of our knowledge, our paper is the first that adapts causal tree methods for learning optimal DTRs. We evaluate our proposed methods using synthetic data and then apply them to real-world data from intensive care units. Our methods outperform state-of-the-art baselines in terms of cumulative regret and percentage of optimal decisions by a considerable margin. Our work improves treatment recommendations from electronic health record and is thus of direct relevance for personalized medicine.

翻译：动态治疗方案（DTRs）在医学中用于根据患者异质性定制序贯治疗决策。然而，当前学习最优DTR的常用方法存在缺陷：它们通常基于结果预测而非治疗效果估计，或使用线性模型，难以处理现代电子健康记录中的复杂患者数据。为解决这些问题，我们提出两种学习最优DTR的新方法，可有效处理复杂患者数据。我们将方法命名为DTR-CT和DTR-CF。这两种方法基于因果树方法（具体包括因果树与因果森林）对异质性治疗效果进行数据驱动估计，能够学习非线性关系、控制时变混杂因素、具备双重稳健性与可解释性。据我们所知，本文是首篇将因果树方法适配至最优DTR学习的研究。我们使用合成数据评估所提方法，并将其应用于重症监护病房的真实临床数据。实验证明，本方法在累积遗憾值与最优决策百分比上均显著优于现有基线模型。本工作改进了基于电子健康记录的治疗建议，对个性化医学具有直接应用价值。