mlsanityData to 14 Oct 2026

Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation

arXiv 1807.01473 ↗First posted 4 Jul 2018cs.LG stat.MLcited by 101

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Cited by 40 in Offline RL and imitation learning from data, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Dynamic treatment recommendation systems based on large-scale electronic health records (EHRs) become a key to successfully improve practical clinical outcomes. Prior relevant studies recommend treatments either use supervised learning (e.g. matching the indicator signal which denotes doctor prescriptions), or reinforcement learning (e.g. maximizing evaluation signal which indicates cumulative reward from survival rates). However, none of these studies have considered to combine the benefits of supervised learning and reinforcement learning. In this paper, we propose Supervised Reinforcement Learning with Recurrent Neural Network (SRL-RNN), which fuses them into a synergistic learning framework. Specifically, SRL-RNN applies an off-policy actor-critic framework to handle complex relations among multiple medications, diseases and individual characteristics. The "actor" in the framework is adjusted by both the indicator signal and evaluation signal to ensure effective prescription and low mortality. RNN is further utilized to solve the Partially-Observed Markov Decision Process (POMDP) problem due to the lack of fully observed states in real world applications. Experiments on the publicly real-world dataset, i.e., MIMIC-3, illustrate that our model can reduce the estimated mortality, while providing promising accuracy in matching doctors' prescriptions.

Key facts. Cited by 101 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 Jul 2018.

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Citing papers per year: 2020: 3; 2021: 16; 2022: 24; 2023: 22; 2024: 16; 2025: 15; 2026: 5.

101 papers.

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  1. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,920
  2. 2020MOReL : Model-Based Offline Reinforcement Learningcited by 590
  3. 2021COMBO: Conservative Offline Model-Based Policy Optimizationcited by 370
  4. 2021Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimismcited by 279
  5. 2020What are the Statistical Limits of Offline RL with Linear Function Approximation?cited by 136
  6. 2022When Should We Prefer Offline Reinforcement Learning Over Behavioral Cloning?cited by 89
  7. 2021Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximationcited by 71
  8. 2021Conservative Data Sharing for Multi-Task Offline Reinforcement Learningcited by 64
  9. 2022How to Leverage Unlabeled Data in Offline Reinforcement Learningcited by 62
  10. 2022Nearly Minimax Optimal Offline Reinforcement Learning with Linear Function Approximation: Single-Agent MDP and Markov Gamecited by 55
  11. 2023Double Pessimism is Provably Efficient for Distributionally Robust Offline Reinforcement Learning: Generic Algorithm and Robust Partial Coveragecited by 51
  12. 2022Pessimistic Minimax Value Iteration: Provably Efficient Equilibrium Learning from Offline Datasetscited by 44
38 more
  1. 2021Instabilities of Offline RL with Pre-Trained Neural Representationcited by 38
  2. 2021Change Matters: Medication Change Prediction with Recurrent Residual Networkscited by 38
  3. 2022Distributionally Robust Offline Reinforcement Learning with Linear Function Approximationcited by 32
  4. 2023Corruption-Robust Offline Reinforcement Learning with General Function Approximationcited by 31
  5. 2022Conditional Generation Net for Medication Recommendationcited by 30
  6. 2024Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithmscited by 26
  7. 2023Offline Imitation Learning with Suboptimal Demonstrations via Relaxed Distribution Matchingcited by 24
  8. 2022Multi-Task Fusion via Reinforcement Learning for Long-Term User Satisfaction in Recommender Systemscited by 22
  9. 2021A Survey of Deep Reinforcement Learning in Recommender Systems: A Systematic Review and Future Directionscited by 21
  10. 2022Double Check Your State Before Trusting It: Confidence-Aware Bidirectional Offline Model-Based Imaginationcited by 21
  11. 2022Confidence-Conditioned Value Functions for Offline Reinforcement Learningcited by 20
  12. 2023A General Offline Reinforcement Learning Framework for Interactive Recommendationcited by 18
  13. 2024Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learningcited by 18
  14. 2025Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategycited by 15
  15. 2024Learning from Imperfect Demonstrations with Self-Supervision for Robotic Manipulationcited by 14
  16. 2023Hundreds Guide Millions: Adaptive Offline Reinforcement Learning with Expert Guidancecited by 13
  17. 2022Behavior Prior Representation learning for Offline Reinforcement Learningcited by 12
  18. 2023A Survey of Demonstration Learningcited by 12
  19. 2021A Survey on Deep Reinforcement Learning for Data Processing and Analyticscited by 10
  20. 2022Provable Sim-to-real Transfer in Continuous Domain with Partial Observationscited by 9
  21. 2025medDreamer: Model-Based Reinforcement Learning with Latent Imagination on Complex EHRs for Clinical Decision Supportcited by 9
  22. 2023Synthesize High-dimensional Longitudinal Electronic Health Records via Hierarchical Autoregressive Language Modelcited by 8
  23. 2023SMURF-THP: Score Matching-based UnceRtainty quantiFication for Transformer Hawkes Processcited by 7
  24. 2023Offline RL with Observation Histories: Analyzing and Improving Sample Complexitycited by 7
  25. 2022Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventionscited by 6
  26. 2023Modeling User Satisfaction Dynamics in Dialogue via Hawkes Processcited by 6
  27. 2023Event-based Dynamic Graph Representation Learning for Patent Application Trend Predictioncited by 6
  28. 2024Compressed Federated Reinforcement Learning with a Generative Modelcited by 6
  29. 2025ORVIT: Near-Optimal Online Distributionally Robust Reinforcement Learningcited by 6
  30. 2022Deconfounding Actor-Critic Network with Policy Adaptation for Dynamic Treatment Regimescited by 5
  31. 2024Learning under Temporal Label Noisecited by 5
  32. 2024Reinforcement Learning in Dynamic Treatment Regimes Needs Critical Reexaminationcited by 5
  33. 2024Mamba Hawkes Processcited by 5
  34. 2022Learning Optimal Dynamic Treatment Regimes Using Causal Tree Methods in Medicinecited by 4
  35. 2024Corruption-Robust Offline Two-Player Zero-Sum Markov Gamescited by 4
  36. 2024Offline Inverse Constrained Reinforcement Learning for Safe-Critical Decision Making in Healthcarecited by 4
  37. 2025Natural Language-Assisted Multi-modal Medication Recommendationcited by 4
  38. 2021Reducing Conservativeness Oriented Offline Reinforcement Learningcited by 3

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