mlsanityData to 7 Oct 2026

Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning

arXiv 1901.04670 ↗First posted 15 Jan 2019cs.LG cs.AI stat.MLcited by 34

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Abstract (arXiv)

Sepsis is the leading cause of mortality in the ICU. It is challenging to manage because individual patients respond differently to treatment. Thus, tailoring treatment to the individual patient is essential for the best outcomes. In this paper, we take steps toward this goal by applying a mixture-of-experts framework to personalize sepsis treatment. The mixture model selectively alternates between neighbor-based (kernel) and deep reinforcement learning (DRL) experts depending on patient's current history. On a large retrospective cohort, this mixture-based approach outperforms physician, kernel only, and DRL-only experts.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Jan 2019.

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Citing papers per year: 2021: 6; 2022: 4; 2023: 6; 2024: 7; 2025: 4; 2026: 7.

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  1. 2021Medical Dead-ends and Learning to Identify High-risk States and Treatmentscited by 28
  2. 2022Semi-Markov Offline Reinforcement Learning for Healthcarecited by 18
  3. 2023Deep Offline Reinforcement Learning for Real-world Treatment Optimization Applicationscited by 17
  4. 2021Unifying Cardiovascular Modelling with Deep Reinforcement Learning for Uncertainty Aware Control of Sepsis Treatmentcited by 16
  5. 2022Towards Safe Mechanical Ventilation Treatment Using Deep Offline Reinforcement Learningcited by 16
  6. 2021Offline reinforcement learning with uncertainty for treatment strategies in sepsiscited by 9
  7. 2025medDreamer: Model-Based Reinforcement Learning with Latent Imagination on Complex EHRs for Clinical Decision Supportcited by 8
  8. 2021Challenges for Reinforcement Learning in Healthcarecited by 7
  9. 2024Reinforced Sequential Decision-Making for Sepsis Treatment: The POSNEGDM Framework with Mortality Classifier and Transformercited by 6
  10. 2022Deconfounding Actor-Critic Network with Policy Adaptation for Dynamic Treatment Regimescited by 5
  11. 2024Reinforcement Learning in Dynamic Treatment Regimes Needs Critical Reexaminationcited by 5
  12. 2024Offline Inverse Constrained Reinforcement Learning for Safe-Critical Decision Making in Healthcarecited by 4
22 more
  1. 2021Reinforcement Learning for Intelligent Healthcare Systems: A Comprehensive Surveycited by 3
  2. 2024Medical Knowledge Integration into Reinforcement Learning Algorithms for Dynamic Treatment Regimescited by 3
  3. 2025Beyond Prediction: Reinforcement Learning as the Defining Leap in Healthcare AIcited by 3
  4. 2026Formally Verifying and Explaining Sepsis Treatment Policies with COOL-MCcited by 3
  5. 2023Pruning the Way to Reliable Policies: A Multi-Objective Deep Q-Learning Approach to Critical Carecited by 2
  6. 2024How Consistent are Clinicians? Evaluating the Predictability of Sepsis Disease Progression with Dynamics Modelscited by 2
  7. 2024Empowering Clinicians with Medical Decision Transformers: A Framework for Sepsis Treatmentcited by 2
  8. 2025Off-Policy Evaluation Under Nonignorable Missing Datacited by 2
  9. 2021Case-based off-policy policy evaluation using prototype learningcited by 1
  10. 2024Identifying Differential Patient Care Through Inverse Intent Inferencecited by 1
  11. 2026The hidden risks of temporal resampling in clinical reinforcement learningcited by 1
  12. 2026EHR-MPC: Inference-Time Control for Sepsis Treatment with Generative Patient Digital Twinscited by 1
  13. 2022Reinforcement Learning For Survival, A Clinically Motivated Method For Critically Ill Patientscited by 0
  14. 2023Deep Attention Q-Network for Personalized Treatment Recommendationcited by 0
  15. 2023Reinforcement Logic Rule Learning for Temporal Point Processescited by 0
  16. 2023Nonparametric Additive Value Functions: Interpretable Reinforcement Learning with an Application to Surgical Recoverycited by 0
  17. 2023Reinforcement learning in large, structured action spaces: A simulation study of decision support for spinal cord injury rehabilitationcited by 0
  18. 2025Learning optimal treatment strategies for intraoperative hypotension using deep reinforcement learningcited by 0
  19. 2026Intelligent Reasoning Cues: A Framework and Case Study of the Roles of AI Information in Complex Decisionscited by 0
  20. 2026medR: Reward Engineering for Clinical Offline Reinforcement Learning via Tri-Drive Potential Functionscited by 0
  21. 2026Learning Dynamic Representations and Policies from Multimodal Clinical Time-Series with Informative Missingnesscited by 0
  22. 2026The Evidence Ladder for Reinforcement Learning in Healthcare: From Retrospective Policies to Trusted Interventionscited by 0

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  1. 2022Learning Optimal Treatment Strategies for Sepsis Using Offline Reinforcement Learning in Continuous Space
  2. 2026Attention-Based Offline Reinforcement Learning and Clustering for Interpretable Sepsis Treatment
  3. 2022A Conservative Q-Learning approach for handling distribution shift in sepsis treatment strategies
  4. 2025SepsisSuite: Beyond Risk Stratification -- A Comparative Analysis of Deep Fusion vs. Expert Stacking for Prescriptive Sepsis AI
  5. 2025Exploring a Graph-based Approach to Offline Reinforcement Learning for Sepsis Treatment
  6. 2026Agentifying Patient Dynamics within LLMs through Interacting with Clinical World Model
  7. 2025Exploring Time-Step Size in Reinforcement Learning for Sepsis Treatment
  8. 2019Sepsis World Model: A MIMIC-based OpenAI Gym "World Model" Simulator for Sepsis Treatment