标题： 动态治疗方案中效用与成本的平衡 显示英文标题

标题： Balancing utility and cost in dynamic treatment regimes

摘要： 动态治疗方案(DTRs)是个性化的、适应性的策略，旨在根据个体特征随时间的变化来指导治疗的顺序分配。 在每次治疗分配之前，会收集协变量信息以优化治疗决策并提高其效果。 我们收集的信息越多，我们的决策就越精确。 然而，这也导致了数据收集阶段的成本增加。 在本工作中，我们提出了一种平衡的Q学习方法，在DTR的效用与治疗分配和协变量评估相关的成本之间取得平衡。 所提出方法的性能通过广泛的数值研究得到验证，包括模拟和对MIMIC-III数据库的真实数据应用。 显示英文摘要

摘要： Dynamic treatment regimes (DTRs) are personalized, adaptive strategies designed to guide the sequential allocation of treatments based on individual characteristics over time. Before each treatment assignment, covariate information is collected to refine treatment decisions and enhance their effectiveness. The more information we gather, the more precise our decisions can be. However, this also leads to higher costs during the data collection phase. In this work, we propose a balanced Q-learning method that strikes a balance between the utility of the DTR and the costs associated with both treatment assignment and covariate assessment. The performance of the proposed method is demonstrated through extensive numerical studies, including simulations and a real-data application to the MIMIC-III database.