mlsanityData to 7 Oct 2026

Medical Knowledge Integration into Reinforcement Learning Algorithms for Dynamic Treatment Regimes

arXiv 2407.00364 ↗First posted 29 Jun 2024stat.ME stat.MLcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 22 of its 115 references that are in the map. First posted on arXiv 29 Jun 2024.

Builds on

22 of 115 references.

References · 22

  1. 2020Artificial Intelligence, Values and Alignmentcited by 443
  2. 2019Reinforcement Learning in Healthcare: A Surveycited by 308
  3. 2018Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendationcited by 101
  4. 2022A Review of Off-Policy Evaluation in Reinforcement Learningcited by 92
  5. 2017Deep Reinforcement Learning for Sepsis Treatmentcited by 71
  6. 2017Continuous State-Space Models for Optimal Sepsis Treatment - a Deep Reinforcement Learning Approachcited by 68
  7. 2016Estimating Dynamic Treatment Regimes in Mobile Health Using V-learningcited by 65
  8. 2021A Survey on Interactive Reinforcement Learning: Design Principles and Open Challengescited by 41
  9. 2019Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learningcited by 34
  10. 2020Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policiescited by 24
  11. 2018Deep Reinforcement Learning for Dynamic Treatment Regimes on Medical Registry Datacited by 23
  12. 2018Model-Based Reinforcement Learning for Sepsis Treatmentcited by 21
10 more
  1. 2020Expert-Supervised Reinforcement Learning for Offline Policy Learning and Evaluationcited by 14
  2. 2019Optimizing Sequential Medical Treatments with Auto-Encoding Heuristic Search in POMDPscited by 12
  3. 2022Optimal regimes for algorithm-assisted human decision-makingcited by 9
  4. 2020Optimizing Medical Treatment for Sepsis in Intensive Care: from Reinforcement Learning to Pre-Trial Evaluationcited by 8
  5. 2014Non-Deterministic Policies in Markovian Decision Processescited by 7
  6. 2018The Actor Search Tree Critic (ASTC) for Off-Policy POMDP Learning in Medical Decision Makingcited by 6
  7. 2022Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventionscited by 6
  8. 2017Estimation and Optimization of Composite Outcomescited by 5
  9. 2022A Conservative Q-Learning approach for handling distribution shift in sepsis treatment strategiescited by 5
  10. 2016Robust Hybrid Learning for Estimating Personalized Dynamic Treatment Regimenscited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Towards Optimal Differentially Private Regret Bounds in Linear MDPscited by 0
  2. 2025Treatment, evidence, imitation, and chatcited by 0
  3. 2026Near-Equivalent Q-learning Policies for Dynamic Treatment Regimescited by 0