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.
@article{arxiv.2407.00364,
title = {Medical Knowledge Integration into Reinforcement Learning Algorithms for Dynamic Treatment Regimes},
author = {Sophia Yazzourh and Nicolas Savy and Philippe Saint-Pierre and Michael R. Kosorok},
journal= {arXiv preprint arXiv:2407.00364},
year = {2025}
}