mlsanityData to 7 Oct 2026

A Reinforcement Learning Approach to Weaning of Mechanical Ventilation in Intensive Care Units

arXiv 1704.06300 ↗First posted 20 Apr 2017cs.AIcited by 50

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Abstract (arXiv)

The management of invasive mechanical ventilation, and the regulation of sedation and analgesia during ventilation, constitutes a major part of the care of patients admitted to intensive care units. Both prolonged dependence on mechanical ventilation and premature extubation are associated with increased risk of complications and higher hospital costs, but clinical opinion on the best protocol for weaning patients off of a ventilator varies. This work aims to develop a decision support tool that uses available patient information to predict time-to-extubation readiness and to recommend a personalized regime of sedation dosage and ventilator support. To this end, we use off-policy reinforcement learning algorithms to determine the best action at a given patient state from sub-optimal historical ICU data. We compare treatment policies from fitted Q-iteration with extremely randomized trees and with feedforward neural networks, and demonstrate that the policies learnt show promise in recommending weaning protocols with improved outcomes, in terms of minimizing rates of reintubation and regulating physiological stability.

Key facts. Cited by 50 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Apr 2017.

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Citing papers per year: 2019: 1; 2020: 1; 2021: 7; 2022: 10; 2023: 8; 2024: 6; 2025: 10; 2026: 7.

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  1. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,873
  2. 2021Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare Settingscited by 68
  3. 2023Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcarecited by 41
  4. 2021Instrumental Variable Value Iteration for Causal Offline Reinforcement Learningcited by 35
  5. 2022Towards Safe Mechanical Ventilation Treatment Using Deep Offline Reinforcement Learningcited by 16
  6. 2022Federated Offline Reinforcement Learningcited by 13
  7. 2023Hundreds Guide Millions: Adaptive Offline Reinforcement Learning with Expert Guidancecited by 13
  8. 2019Inverse Reinforcement Learning in Contextual MDPscited by 12
  9. 2021Machine Learning for Mechanical Ventilation Controlcited by 8
  10. 2021Challenges for Reinforcement Learning in Healthcarecited by 7
  11. 2022Reinforcement Learning with Heterogeneous Data: Estimation and Inferencecited by 7
  12. 2023Risk Sensitive Dead-end Identification in Safety-Critical Offline Reinforcement Learningcited by 6
38 more
  1. 2022Reinforcement Learning in Practice: Opportunities and Challengescited by 5
  2. 2022A Conservative Q-Learning approach for handling distribution shift in sepsis treatment strategiescited by 5
  3. 2022Deconfounding Actor-Critic Network with Policy Adaptation for Dynamic Treatment Regimescited by 5
  4. 2025CaRT: Teaching LLM Agents to Know When They Know Enoughcited by 5
  5. 2022Learning Optimal Dynamic Treatment Regimes Using Causal Tree Methods in Medicinecited by 4
  6. 2022Learning Optimal Treatment Strategies for Sepsis Using Offline Reinforcement Learning in Continuous Spacecited by 4
  7. 2025Time-Aware World Model for Adaptive Prediction and Controlcited by 4
  8. 2022Transfer Q-learningcited by 3
  9. 2022Predicting the Need for Blood Transfusion in Intensive Care Units with Reinforcement Learningcited by 3
  10. 2023Leveraging Factored Action Spaces for Off-Policy Evaluationcited by 3
  11. 2025Advancing Safe Mechanical Ventilation Using Offline RL With Hybrid Actions and Clinically Aligned Rewardscited by 3
  12. 2025Beyond Prediction: Reinforcement Learning as the Defining Leap in Healthcare AIcited by 3
  13. 2023A Unified Framework of Policy Learning for Contextual Bandit with Confounding Bias and Missing Observationscited by 2
  14. 2023Pruning the Way to Reliable Policies: A Multi-Objective Deep Q-Learning Approach to Critical Carecited by 2
  15. 2024Measurement Scheduling for ICU Patients with Offline Reinforcement Learningcited by 2
  16. 2024Data-Driven Simulator for Mechanical Circulatory Support with Domain Adversarial Neural Processcited by 2
  17. 2025NeoRL-2: Near Real-World Benchmarks for Offline Reinforcement Learning with Extended Realistic Scenarioscited by 2
  18. 2025Off-Policy Evaluation Under Nonignorable Missing Datacited by 2
  19. 2025Guardian-regularized Safe Offline Reinforcement Learning for Smart Weaning of Mechanical Circulatory Devicescited by 2
  20. 2021Evaluating the progress of Deep Reinforcement Learning in the real world: aligning domain-agnostic and domain-specific researchcited by 1
  21. 2024PAIL: Performance based Adversarial Imitation Learning Engine for Carbon Neutral Optimizationcited by 1
  22. 2024Robust Real-Time Mortality Prediction in the Intensive Care Unit using Temporal Difference Learningcited by 1
  23. 2024Identifying Differential Patient Care Through Inverse Intent Inferencecited by 1
  24. 2025Adaptive Scaling of Policy Constraints for Offline Reinforcement Learningcited by 1
  25. 2026The hidden risks of temporal resampling in clinical reinforcement learningcited by 1
  26. 2021Reinforcement Learning Assisted Oxygen Therapy for COVID-19 Patients Under Intensive Carecited by 0
  27. 2021Machine Learning for Mechanical Ventilation Control (Extended Abstract)cited by 0
  28. 2023Deep Attention Q-Network for Personalized Treatment Recommendationcited by 0
  29. 2023Reinforcement learning in large, structured action spaces: A simulation study of decision support for spinal cord injury rehabilitationcited by 0
  30. 2024Upside-Down Reinforcement Learning for More Interpretable Optimal Controlcited by 0
  31. 2025Learning optimal treatment strategies for intraoperative hypotension using deep reinforcement learningcited by 0
  32. 2025Using AI to Optimize Patient Transfer and Resource Utilization During Mass-Casualty Incidents: A Simulation Platformcited by 0
  33. 2026On Safer Reinforcement Learning for Sedation and Analgesia in Intensive Carecited by 0
  34. 2026Learning to Compress Time-to-Control: A Reinforcement Learning Framework for Chronic Disease Managementcited by 0
  35. 2026VentAgent: When LLMs Learn to Breathe -- Multi-Objective Arbitration for ARDS Ventilationcited by 0
  36. 2026Evaluating covariate balance for long time horizon Markov decision processescited by 0
  37. 2026Removing Temporal Note Redundancy Improves Multimodal Reinforcement Learning for Medicinecited by 0
  38. 2026The Evidence Ladder for Reinforcement Learning in Healthcare: From Retrospective Policies to Trusted Interventionscited by 0