Medical Knowledge Integration into Reinforcement Learning Algorithms for Dynamic Treatment Regimes

TL;DR

This paper explores how reinforcement learning can be combined with medical knowledge to improve personalized treatment strategies in chronic disease management, enhancing confidence and adoption in clinical settings.

Contribution

It provides a mathematical foundation for RL in DTR and reviews methods for integrating medical expertise to improve treatment decision algorithms.

Findings

Enhanced treatment decision confidence through medical knowledge integration

Frameworks for combining RL with clinical expertise

Potential for increased adoption of RL-based DTR in healthcare

Abstract

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.