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Medical Knowledge Integration Into Reinforcement Learning Algorithms for Dynamic Treatment Regimes

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  • Sophia Yazzourh
  • Nicolas Savy
  • Philippe Saint‐Pierre
  • Michael R. Kosorok

Abstract

The goal of precision medicine is to provide individualised treatment at each stage of chronic diseases, a concept formalised 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, contextualise its application in the field of DTR, and present an overview of methods to improve its effectiveness by integrating medical expertise.

Suggested Citation

  • Sophia Yazzourh & Nicolas Savy & Philippe Saint‐Pierre & Michael R. Kosorok, 2026. "Medical Knowledge Integration Into Reinforcement Learning Algorithms for Dynamic Treatment Regimes," International Statistical Review, International Statistical Institute, vol. 94(2), pages 382-411, August.
    • Handle: RePEc:bla:istatr:v:94:y:2026:i:2:p:382-411
    DOI: 10.1111/insr.12617
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