The goal of precision medicine is to provide individualized treatment at each stage of chronic diseases, a concept formalized by Dynamic Treatment Regimes (DTR). These regimes adapt treatment strategies based on decision rules learned from clinical data to enhance therapeutic effectiveness. Reinforcement Learning (RL) algorithms allow to determine these decision rules conditioned by individual patient data and their medical history. The integration of medical expertise into these models makes possible to increase confidence in treatment recommendations and facilitate the adoption of this approach by healthcare professionals and patients. In this work, we examine the mathematical foundations of RL, contextualize its application in the field of DTR, and present an overview of methods to improve its effectiveness by integrating medical expertise.

翻译：精准医疗的目标是在慢性疾病的每个阶段提供个体化治疗，这一概念通过动态治疗策略（DTR）得以形式化。这些策略基于从临床数据中学习到的决策规则来调整治疗方案，以提高治疗效果。强化学习（RL）算法能够根据个体患者数据及其病史来确定这些决策规则。将医学专业知识融入这些模型，可以增强对治疗建议的信心，并促进医疗专业人员和患者采纳这种方法。在本研究中，我们探讨了强化学习的数学基础，阐述了其在动态治疗策略领域的应用背景，并概述了通过整合医学专业知识来提高其有效性的方法。