mlsanityData to 14 Oct 2026

Deep Reinforcement Learning for Sepsis Treatment

arXiv 1711.09602 ↗First posted 27 Nov 2017cs.AI cs.LGcited by 71

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Abstract (arXiv)

Sepsis is a leading cause of mortality in intensive care units and costs hospitals billions annually. Treating a septic patient is highly challenging, because individual patients respond very differently to medical interventions and there is no universally agreed-upon treatment for sepsis. In this work, we propose an approach to deduce treatment policies for septic patients by using continuous state-space models and deep reinforcement learning. Our model learns clinically interpretable treatment policies, similar in important aspects to the treatment policies of physicians. The learned policies could be used to aid intensive care clinicians in medical decision making and improve the likelihood of patient survival.

Key facts. Cited by 71 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Nov 2017.

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Citing papers per year: 2019: 1; 2020: 1; 2021: 7; 2022: 13; 2023: 16; 2024: 16; 2025: 11; 2026: 6.

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  1. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,920
  2. 2021Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare Settingscited by 68
  3. 2022The Health Gym: Synthetic Health-Related Datasets for the Development of Reinforcement Learning Algorithmscited by 19
  4. 2023Deep Offline Reinforcement Learning for Real-world Treatment Optimization Applicationscited by 17
  5. 2021Unifying Cardiovascular Modelling with Deep Reinforcement Learning for Uncertainty Aware Control of Sepsis Treatmentcited by 16
  6. 2022Towards Safe Mechanical Ventilation Treatment Using Deep Offline Reinforcement Learningcited by 16
  7. 2023Robust Fitted-Q-Evaluation and Iteration under Sequentially Exogenous Unobserved Confounderscited by 13
  8. 2023Finding Counterfactually Optimal Action Sequences in Continuous State Spacescited by 13
  9. 2023Hundreds Guide Millions: Adaptive Offline Reinforcement Learning with Expert Guidancecited by 13
  10. 2019Inverse Reinforcement Learning in Contextual MDPscited by 12
  11. 2023A Survey of Demonstration Learningcited by 12
  12. 2023Synthetic Health-related Longitudinal Data with Mixed-type Variables Generated using Diffusion Modelscited by 11
38 more
  1. 2025medDreamer: Model-Based Reinforcement Learning with Latent Imagination on Complex EHRs for Clinical Decision Supportcited by 9
  2. 2023On the Importance of Feature Decorrelation for Unsupervised Representation Learning in Reinforcement Learningcited by 8
  3. 2021Challenges for Reinforcement Learning in Healthcarecited by 7
  4. 2022Optimizing Pessimism in Dynamic Treatment Regimes: A Bayesian Learning Approachcited by 7
  5. 2023Quasi-optimal Reinforcement Learning with Continuous Actionscited by 7
  6. 2023Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confoundingcited by 7
  7. 2023Off-policy Evaluation in Doubly Inhomogeneous Environmentscited by 7
  8. 2023Imitate the Good and Avoid the Bad: An Incremental Approach to Safe Reinforcement Learningcited by 7
  9. 2024SPRINQL: Sub-optimal Demonstrations driven Offline Imitation Learningcited by 7
  10. 2022Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventionscited by 6
  11. 2024ICU-Sepsis: A Benchmark MDP Built from Real Medical Datacited by 6
  12. 2022A Conservative Q-Learning approach for handling distribution shift in sepsis treatment strategiescited by 5
  13. 2022Deconfounding Actor-Critic Network with Policy Adaptation for Dynamic Treatment Regimescited by 5
  14. 2023HOPE: Human-Centric Off-Policy Evaluation for E-Learning and Healthcarecited by 5
  15. 2024Reinforcement Learning in Dynamic Treatment Regimes Needs Critical Reexaminationcited by 5
  16. 2022Learning Optimal Dynamic Treatment Regimes Using Causal Tree Methods in Medicinecited by 4
  17. 2022Learning Optimal Treatment Strategies for Sepsis Using Offline Reinforcement Learning in Continuous Spacecited by 4
  18. 2024Finite-Time Convergence and Sample Complexity of Actor-Critic Multi-Objective Reinforcement Learningcited by 4
  19. 2024Advancing Multi-Organ Disease Care: A Hierarchical Multi-Agent Reinforcement Learning Frameworkcited by 4
  20. 2024Offline Inverse Constrained Reinforcement Learning for Safe-Critical Decision Making in Healthcarecited by 4
  21. 2025Reinforcement Learning for Individual Optimal Policy from Heterogeneous Datacited by 4
  22. 2021Discovering an Aid Policy to Minimize Student Evasion Using Offline Reinforcement Learningcited by 3
  23. 2021Reinforcement Learning for Intelligent Healthcare Systems: A Comprehensive Surveycited by 3
  24. 2022Predicting the Need for Blood Transfusion in Intensive Care Units with Reinforcement Learningcited by 3
  25. 2022Practical Approaches for Fair Learning with Multitype and Multivariate Sensitive Attributescited by 3
  26. 2024TEE4EHR: Transformer Event Encoder for Better Representation Learning in Electronic Health Recordscited by 3
  27. 2024Counterfactual Reasoning Using Predicted Latent Personality Dimensions for Optimizing Persuasion Outcomecited by 3
  28. 2024Preference Elicitation for Offline Reinforcement Learningcited by 3
  29. 2024Medical Knowledge Integration into Reinforcement Learning Algorithms for Dynamic Treatment Regimescited by 3
  30. 2025Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Datacited by 3
  31. 2025Causal Discovery and Counterfactual Reasoning to Optimize Persuasive Dialogue Policiescited by 3
  32. 2021InferNet for Delayed Reinforcement Tasks: Addressing the Temporal Credit Assignment Problemcited by 2
  33. 2024Empowering Clinicians with Medical Decision Transformers: A Framework for Sepsis Treatmentcited by 2
  34. 2025Generative Framework for Personalized Persuasion: Inferring Causal, Counterfactual, and Latent Knowledgecited by 2
  35. 2025Off-Policy Evaluation Under Nonignorable Missing Datacited by 2
  36. 2025MIMIC-Sepsis: A Curated Benchmark for Modeling and Learning from Sepsis Trajectories in the ICUcited by 2
  37. 2026Agentifying Patient Dynamics within LLMs through Interacting with Clinical World Modelcited by 2
  38. 2026EHR-MPC: Inference-Time Control for Sepsis Treatment with Generative Patient Digital Twinscited by 2

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