mlsanityData to 7 Oct 2026

Towards Safe Mechanical Ventilation Treatment Using Deep Offline Reinforcement Learning

arXiv 2210.02552 ↗First posted 5 Oct 2022cs.LGcited by 16

On the map: Offline RL and imitation learning from data → Safe offline RL

Abstract (arXiv)

Mechanical ventilation is a key form of life support for patients with pulmonary impairment. Healthcare workers are required to continuously adjust ventilator settings for each patient, a challenging and time consuming task. Hence, it would be beneficial to develop an automated decision support tool to optimize ventilation treatment. We present DeepVent, a Conservative Q-Learning (CQL) based offline Deep Reinforcement Learning (DRL) agent that learns to predict the optimal ventilator parameters for a patient to promote 90 day survival. We design a clinically relevant intermediate reward that encourages continuous improvement of the patient vitals as well as addresses the challenge of sparse reward in RL. We find that DeepVent recommends ventilation parameters within safe ranges, as outlined in recent clinical trials. The CQL algorithm offers additional safety by mitigating the overestimation of the value estimates of out-of-distribution states/actions. We evaluate our agent using Fitted Q Evaluation (FQE) and demonstrate that it outperforms physicians from the MIMIC-III dataset.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Placed in Safe offline RL, one of 13 directions in Offline RL and imitation learning from data. Builds on 10 of its 32 references that are in the map. First posted on arXiv 5 Oct 2022.

Builds on

10 of 32 references.

Closest ancestors · 1

Same direction, newest first

  1. 2021Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare Settings23 Jul

Other references · 9

  1. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,873
  2. 2020Conservative Q-Learning for Offline Reinforcement Learningcited by 1,777
  3. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,704
  4. 2019Reinforcement Learning in Healthcare: A Surveycited by 308
  5. 2019Batch Policy Learning under Constraintscited by 242
  6. 2017Deep Reinforcement Learning for Sepsis Treatmentcited by 71
  7. 2017A Reinforcement Learning Approach to Weaning of Mechanical Ventilation in Intensive Care Unitscited by 50
  8. 2019Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learningcited by 34
  9. 2021Machine Learning for Mechanical Ventilation Controlcited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 4; 2025: 5; 2026: 4.

16 papers.

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  1. 2024Temporal Logic Specification-Conditioned Decision Transformer for Offline Safe Reinforcement Learningcited by 10
  2. 2025medDreamer: Model-Based Reinforcement Learning with Latent Imagination on Complex EHRs for Clinical Decision Supportcited by 8
  3. 2024Reinforcement Learning in Dynamic Treatment Regimes Needs Critical Reexaminationcited by 5
  4. 2024Offline Inverse Constrained Reinforcement Learning for Safe-Critical Decision Making in Healthcarecited by 4
  5. 2025Beyond Prediction: Reinforcement Learning as the Defining Leap in Healthcare AIcited by 3
  6. 2023Towards Real-World Applications of Personalized Anesthesia Using Policy Constraint Q Learning for Propofol Infusion Controlcited by 2
  7. 2023Safe and Interpretable Estimation of Optimal Treatment Regimescited by 1
  8. 2024Methodology for Interpretable Reinforcement Learning for Optimizing Mechanical Ventilationcited by 1
  9. 2025Unmasking Societal Biases in Respiratory Support for ICU Patients through Social Determinants of Healthcited by 1
  10. 2026The hidden risks of temporal resampling in clinical reinforcement learningcited by 1
  11. 2023The Safety Challenges of Deep Learning in Real-World Type 1 Diabetes Managementcited by 0
  12. 2025ExOSITO: Explainable Off-Policy Learning with Side Information for Intensive Care Unit Blood Test Orderscited by 0
4 more
  1. 2025Inferring Relative Consequences of Mechanical Ventilation from Observational Data Using Game-Based Comparisonscited by 0
  2. 2026Ensuring Safety in Automated Mechanical Ventilation through Offline Reinforcement Learning and Digital Twin Verificationcited by 0
  3. 2026VentAgent: When LLMs Learn to Breathe -- Multi-Objective Arbitration for ARDS Ventilationcited by 0
  4. 2026Evaluating covariate balance for long time horizon Markov decision processescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Advancing Safe Mechanical Ventilation Using Offline RL With Hybrid Actions and Clinically Aligned Rewards
  2. 2023Pruning the Way to Reliable Policies: A Multi-Objective Deep Q-Learning Approach to Critical Care
  3. 2023Deep Offline Reinforcement Learning for Real-world Treatment Optimization Applications
  4. 2022A Conservative Q-Learning approach for handling distribution shift in sepsis treatment strategies
  5. 2022Learning Optimal Treatment Strategies for Sepsis Using Offline Reinforcement Learning in Continuous Space
  6. 2025Benchmarking Offline Multi-Objective Reinforcement Learning in Critical Care
  7. 2025Guardian-regularized Safe Offline Reinforcement Learning for Smart Weaning of Mechanical Circulatory Devices
  8. 2026Human-in-the-Loop Multi-Agent Ventilator Decision Support with Contextual Bandit Preference Learning