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

arXiv: https://arxiv.org/abs/1704.06300 · HTML: https://mlsanity.com/paper/1704.06300/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2017.

## Built on by (most cited first, 30 of 50)

- [Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems](https://mlsanity.com/paper/2005.01643/) (2020)
- [Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare Settings](https://mlsanity.com/paper/2107.11003/) (2021)
- [Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare](https://mlsanity.com/paper/2305.01738/) (2023)
- [Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning](https://mlsanity.com/paper/2102.09907/) (2021)
- [Towards Safe Mechanical Ventilation Treatment Using Deep Offline Reinforcement Learning](https://mlsanity.com/paper/2210.02552/) (2022)
- [Federated Offline Reinforcement Learning](https://mlsanity.com/paper/2206.05581/) (2022)
- [Hundreds Guide Millions: Adaptive Offline Reinforcement Learning with Expert Guidance](https://mlsanity.com/paper/2309.01448/) (2023)
- [Inverse Reinforcement Learning in Contextual MDPs](https://mlsanity.com/paper/1905.09710/) (2019)
- [Machine Learning for Mechanical Ventilation Control](https://mlsanity.com/paper/2102.06779/) (2021)
- [Challenges for Reinforcement Learning in Healthcare](https://mlsanity.com/paper/2103.05612/) (2021)
- [Reinforcement Learning with Heterogeneous Data: Estimation and Inference](https://mlsanity.com/paper/2202.00088/) (2022)
- [CaRT: Teaching LLM Agents to Know When They Know Enough](https://mlsanity.com/paper/2510.08517/) (2025)
- [Reinforcement Learning in Practice: Opportunities and Challenges](https://mlsanity.com/paper/2202.11296/) (2022)
- [Risk Sensitive Dead-end Identification in Safety-Critical Offline Reinforcement Learning](https://mlsanity.com/paper/2301.05664/) (2023)
- [A Conservative Q-Learning approach for handling distribution shift in sepsis treatment strategies](https://mlsanity.com/paper/2203.13884/) (2022)
- [Deconfounding Actor-Critic Network with Policy Adaptation for Dynamic Treatment Regimes](https://mlsanity.com/paper/2205.09852/) (2022)
- [Learning Optimal Dynamic Treatment Regimes Using Causal Tree Methods in Medicine](https://mlsanity.com/paper/2204.07124/) (2022)
- [Learning Optimal Treatment Strategies for Sepsis Using Offline Reinforcement Learning in Continuous Space](https://mlsanity.com/paper/2206.11190/) (2022)
- [Time-Aware World Model for Adaptive Prediction and Control](https://mlsanity.com/paper/2506.08441/) (2025)
- [Transfer Q-learning](https://mlsanity.com/paper/2202.04709/) (2022)
- [Predicting the Need for Blood Transfusion in Intensive Care Units with Reinforcement Learning](https://mlsanity.com/paper/2206.14198/) (2022)
- [Leveraging Factored Action Spaces for Off-Policy Evaluation](https://mlsanity.com/paper/2307.07014/) (2023)
- [Measurement Scheduling for ICU Patients with Offline Reinforcement Learning](https://mlsanity.com/paper/2402.07344/) (2024)
- [Advancing Safe Mechanical Ventilation Using Offline RL With Hybrid Actions and Clinically Aligned Rewards](https://mlsanity.com/paper/2506.14375/) (2025)
- [Beyond Prediction: Reinforcement Learning as the Defining Leap in Healthcare AI](https://mlsanity.com/paper/2508.21101/) (2025)
- [A Unified Framework of Policy Learning for Contextual Bandit with Confounding Bias and Missing Observations](https://mlsanity.com/paper/2303.11187/) (2023)
- [Pruning the Way to Reliable Policies: A Multi-Objective Deep Q-Learning Approach to Critical Care](https://mlsanity.com/paper/2306.08044/) (2023)
- [Data-Driven Simulator for Mechanical Circulatory Support with Domain Adversarial Neural Process](https://mlsanity.com/paper/2405.18536/) (2024)
- [NeoRL-2: Near Real-World Benchmarks for Offline Reinforcement Learning with Extended Realistic Scenarios](https://mlsanity.com/paper/2503.19267/) (2025)
- [Off-Policy Evaluation Under Nonignorable Missing Data](https://mlsanity.com/paper/2507.06961/) (2025)

## 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.