mlsanityData to 7 Oct 2026

A Conservative Q-Learning approach for handling distribution shift in sepsis treatment strategies

arXiv 2203.13884 ↗First posted 25 Mar 2022cs.LGcited by 5

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Abstract (arXiv)

Sepsis is a leading cause of mortality and its treatment is very expensive. Sepsis treatment is also very challenging because there is no consensus on what interventions work best and different patients respond very differently to the same treatment. Deep Reinforcement Learning methods can be used to come up with optimal policies for treatment strategies mirroring physician actions. In the healthcare scenario, the available data is mostly collected offline with no interaction with the environment, which necessitates the use of offline RL techniques. The Offline RL paradigm suffers from action distribution shifts which in turn negatively affects learning an optimal policy for the treatment. In this work, a Conservative-Q Learning (CQL) algorithm is used to mitigate this shift and its corresponding policy reaches closer to the physicians policy than conventional deep Q Learning. The policy learned could help clinicians in Intensive Care Units to make better decisions while treating septic patients and improve survival rate.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 3 of its 23 references that are in the map. First posted on arXiv 25 Mar 2022.

Builds on

3 of 23 references.

References · 3

  1. 2020Conservative Q-Learning for Offline Reinforcement Learningcited by 1,777
  2. 2017Deep Reinforcement Learning for Sepsis Treatmentcited by 71
  3. 2017A Reinforcement Learning Approach to Weaning of Mechanical Ventilation in Intensive Care Unitscited by 50

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 2; 2026: 1.

5 papers.

  1. 2024Reinforced Sequential Decision-Making for Sepsis Treatment: The POSNEGDM Framework with Mortality Classifier and Transformercited by 6
  2. 2024Medical Knowledge Integration into Reinforcement Learning Algorithms for Dynamic Treatment Regimescited by 3
  3. 2025MORE-CLEAR: Multimodal Offline Reinforcement learning for Clinical notes Leveraged Enhanced State Representationcited by 2
  4. 2025DeepEN: A Deep Reinforcement Learning Framework for Personalized Enteral Nutrition in Critical Carecited by 0
  5. 2026Evaluating covariate balance for long time horizon Markov decision processescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2022Learning Optimal Treatment Strategies for Sepsis Using Offline Reinforcement Learning in Continuous Space
  2. 2021Offline reinforcement learning with uncertainty for treatment strategies in sepsis
  3. 2023Deep Offline Reinforcement Learning for Real-world Treatment Optimization Applications
  4. 2024Identifying Differential Patient Care Through Inverse Intent Inference
  5. 2024Empowering Clinicians with Medical Decision Transformers: A Framework for Sepsis Treatment
  6. 2026Attention-Based Offline Reinforcement Learning and Clustering for Interpretable Sepsis Treatment
  7. 2024Reinforcement Learning in Dynamic Treatment Regimes Needs Critical Reexamination
  8. 2019Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning