mlsanityData to 14 Oct 2026

On Safer Reinforcement Learning for Sedation and Analgesia in Intensive Care

arXiv 2601.23154 ↗First posted 30 Jan 2026cs.LG cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

Pain management in intensive care usually involves complex trade-offs, since both inadequate and excessive treatment can compromise patient safety. Prior work on reinforcement learning for sedation and analgesia has explored how to optimize these interventions, but has not considered patient survival or partial observability. To investigate the risks of these design choices, we developed an offline deep reinforcement learning framework that suggests hourly medication doses based on recurrent state representations. Using retrospective data from 47,144 ICU stays in the MIMIC-IV database, we trained and evaluated behavior-regularized actor-critic models that prescribe continuous doses of opioids, propofol, benzodiazepines, and dexmedetomidine according to two goals: reduce pain or jointly reduce pain and 30-day post-discharge mortality. Although the two resulting policies were associated with lower pain, clinician agreement with the pain-only policy was positively correlated with mortality (ρ=0.119, p<0.0001), while agreement with the joint policy was negatively correlated (ρ=-0.316, p<0.0001). We found that such divergence arose from a different response to high levels of comorbidity. This suggests that valuing post-discharge outcomes could be critical for learning safer treatment policies, even if a short-term goal remains the primary objective.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 49 references that are in the map. First posted on arXiv 30 Jan 2026.

Builds on

12 of 49 references.

References · 12

  1. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  2. 2015Continuous control with deep reinforcement learningcited by 3,407
  3. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  4. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,920
  5. 2020Conservative Q-Learning for Offline Reinforcement Learningcited by 1,809
  6. 2014On the Properties of Neural Machine Translation: Encoder-Decoder Approachescited by 1,304
  7. 2018Off-Policy Deep Reinforcement Learning without Explorationcited by 1,241
  8. 2021A Minimalist Approach to Offline Reinforcement Learningcited by 899
  9. 2019Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reductioncited by 840
  10. 2017A Reinforcement Learning Approach to Weaning of Mechanical Ventilation in Intensive Care Unitscited by 50
  11. 2022An Extensive Data Processing Pipeline for MIMIC-IVcited by 40
  12. 2019Deep Reinforcement Learning for Optimal Critical Care Pain Management with Morphine using Dueling Double-Deep Q Networkscited by 8

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026Offline Reinforcement Learning for Hemodynamic Management of Sepsis in the ICU: a MIMIC-IV Study with Dual Off-Policy Evaluation
  2. 2025Realistic CDSS Drug Dosing with End-to-end Recurrent Q-learning for Dual Vasopressor Control
  3. 2023Pruning the Way to Reliable Policies: A Multi-Objective Deep Q-Learning Approach to Critical Care
  4. 2025Learning optimal treatment strategies for intraoperative hypotension using deep reinforcement learning
  5. 2022Learning Optimal Treatment Strategies for Sepsis Using Offline Reinforcement Learning in Continuous Space
  6. 2024Development and Validation of Heparin Dosing Policies Using an Offline Reinforcement Learning Algorithm
  7. 2025Multi-Agent Deep Reinforcement Learning for Multiple Anesthetics Collaborative Control ↗
  8. 2026Unmasking Toxic Mimicry in Medical Offline Reinforcement Learning for ICU Sepsis Management via Counterfactual Clinical Audits