mlsanityData to 14 Oct 2026

Model-Based Reinforcement Learning for Sepsis Treatment

arXiv 1811.09602 ↗First posted 23 Nov 2018cs.LG cs.AI stat.MLcited by 21

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Abstract (arXiv)

Sepsis is a dangerous condition that is a leading cause of patient mortality. Treating sepsis 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 explore the use of continuous state-space model-based reinforcement learning (RL) to discover high-quality treatment policies for sepsis patients. Our quantitative evaluation reveals that by blending the treatment strategy discovered with RL with what clinicians follow, we can obtain improved policies, potentially allowing for better medical treatment for sepsis.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 Nov 2018.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 2; 2022: 0; 2023: 4; 2024: 7; 2025: 3; 2026: 5.

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  1. 2023Robust Fitted-Q-Evaluation and Iteration under Sequentially Exogenous Unobserved Confounderscited by 13
  2. 2025A Survey of Safe Reinforcement Learning and Constrained MDPs: A Technical Survey on Single-Agent and Multi-Agent Safetycited by 13
  3. 2025medDreamer: Model-Based Reinforcement Learning with Latent Imagination on Complex EHRs for Clinical Decision Supportcited by 9
  4. 2021Challenges for Reinforcement Learning in Healthcarecited by 7
  5. 2021Feature-Based Interpretable Reinforcement Learning based on State-Transition Modelscited by 6
  6. 2024Reinforced Sequential Decision-Making for Sepsis Treatment: The POSNEGDM Framework with Mortality Classifier and Transformercited by 6
  7. 2024Reinforcement Learning in Dynamic Treatment Regimes Needs Critical Reexaminationcited by 5
  8. 2024Advancing Multi-Organ Disease Care: A Hierarchical Multi-Agent Reinforcement Learning Frameworkcited by 4
  9. 2024Medical Knowledge Integration into Reinforcement Learning Algorithms for Dynamic Treatment Regimescited by 3
  10. 2023Pruning the Way to Reliable Policies: A Multi-Objective Deep Q-Learning Approach to Critical Carecited by 2
  11. 2024Empowering Clinicians with Medical Decision Transformers: A Framework for Sepsis Treatmentcited by 2
  12. 2025Off-Policy Evaluation Under Nonignorable Missing Datacited by 2
9 more
  1. 2026Agentifying Patient Dynamics within LLMs through Interacting with Clinical World Modelcited by 2
  2. 2026EHR-MPC: Inference-Time Control for Sepsis Treatment with Generative Patient Digital Twinscited by 2
  3. 2023A optimization framework for herbal prescription planning based on deep reinforcement learningcited by 1
  4. 2024Development and Validation of Heparin Dosing Policies Using an Offline Reinforcement Learning Algorithmcited by 1
  5. 2024Identifying Differential Patient Care Through Inverse Intent Inferencecited by 1
  6. 2023Deep Attention Q-Network for Personalized Treatment Recommendationcited by 0
  7. 2026medR: Reward Engineering for Clinical Offline Reinforcement Learning via Tri-Drive Potential Functionscited by 0
  8. 2026Learning Dynamic Representations and Policies from Multimodal Clinical Time-Series with Informative Missingnesscited by 0
  9. 2026Evaluating covariate balance for long time horizon Markov decision processescited by 0