Multiobjective tree-based reinforcement learning for estimating tolerant dynamic treatment regimes.

ABSTRACT: A dynamic treatment regime (DTR) is a sequence of treatment decision rules that dictate individualized treatments based on evolving treatment and covariate history. It provides a vehicle for optimizing a clinical decision support system and fits well into the broader paradigm of personalized medicine. However, many real-world problems involve multiple competing priorities, and decision rules differ when trade-offs are present. Correspondingly, there may be more than one feasible decision that leads to empirically sufficient optimization. In this paper, we propose a concept of "tolerant regime," which provides a set of individualized feasible decision rules under a prespecified tolerance rate. A multiobjective tree-based reinforcement learning (MOT-RL) method is developed to directly estima

SUBMITTER: Song Y 

PROVIDER: S-EPMC10871869 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature