mlsanityData to 14 Oct 2026

Challenges for Reinforcement Learning in Healthcare

arXiv 2103.05612 ↗First posted 9 Mar 2021cs.LG cs.AIcited by 7

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Abstract (arXiv)

Many healthcare decisions involve navigating through a multitude of treatment options in a sequential and iterative manner to find an optimal treatment pathway with the goal of an optimal patient outcome. Such optimization problems may be amenable to reinforcement learning. A reinforcement learning agent could be trained to provide treatment recommendations for physicians, acting as a decision support tool. However, a number of difficulties arise when using RL beyond benchmark environments, such as specifying the reward function, choosing an appropriate state representation and evaluating the learned policy.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 16 of its 54 references that are in the map. First posted on arXiv 9 Mar 2021.

Builds on

16 of 54 references.

References · 16

  1. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  2. 2018Off-Policy Deep Reinforcement Learning without Explorationcited by 1,241
  3. 2015Doubly Robust Off-policy Value Evaluation for Reinforcement Learningcited by 339
  4. 2018Learning to Explain: An Information-Theoretic Perspective on Model Interpretationcited by 248
  5. 2018More Robust Doubly Robust Off-policy Evaluationcited by 141
  6. 2019Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Modelscited by 117
  7. 2017Deep Reinforcement Learning for Sepsis Treatmentcited by 71
  8. 2017Continuous State-Space Models for Optimal Sepsis Treatment - a Deep Reinforcement Learning Approachcited by 68
  9. 2018Evaluating Reinforcement Learning Algorithms in Observational Health Settingscited by 60
  10. 2017A Reinforcement Learning Approach to Weaning of Mechanical Ventilation in Intensive Care Unitscited by 50
  11. 2019Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learningcited by 34
  12. 2018Patient2Vec: A Personalized Interpretable Deep Representation of the Longitudinal Electronic Health Recordcited by 30
4 more
  1. 2018Model-Based Reinforcement Learning for Sepsis Treatmentcited by 21
  2. 2017Representation and Reinforcement Learning for Personalized Glycemic Control in Septic Patientscited by 13
  3. 2018An Optimal Policy for Patient Laboratory Tests in Intensive Care Unitscited by 7
  4. 2016Introduction to the "Industrial Benchmark"cited by 3

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 3; 2024: 2; 2025: 1.

7 papers.

  1. 2022Redefining Counterfactual Explanations for Reinforcement Learning: Overview, Challenges and Opportunitiescited by 13
  2. 2023Uncertainty-driven Trajectory Truncation for Data Augmentation in Offline Reinforcement Learningcited by 13
  3. 2025Beyond Prediction: Reinforcement Learning as the Defining Leap in Healthcare AIcited by 3
  4. 2024OCMDP: Observation-Constrained Markov Decision Processcited by 2
  5. 2023Policy Resilience to Environment Poisoning Attacks on Reinforcement Learningcited by 1
  6. 2023Safe and Interpretable Estimation of Optimal Treatment Regimescited by 1
  7. 2024Inverse Reinforcement Learning with Multiple Planning Horizonscited by 0