标题： 将医学知识整合到动态治疗方案的强化学习算法中 显示英文标题

标题： Medical Knowledge Integration into Reinforcement Learning Algorithms for Dynamic Treatment Regimes

摘要： 精准医学的目标是在慢性病的每个阶段提供个性化的治疗方案，这一概念由动态治疗方案（DTR）正式提出。 这些方案根据从临床数据中学到的决策规则来调整治疗策略，以提高治疗效果。 强化学习（RL）算法可以确定基于个体患者数据及其病史的决策规则。 将医学专业知识整合到这些模型中，能够增强对治疗建议的信心，并促进医疗专业人员和患者对该方法的采纳。 在这项工作中，我们研究了RL的数学基础，将其应用于DTR领域进行了情境化，并概述了通过整合医学专业知识来提升其效果的方法。 显示英文摘要

摘要： 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.