ArticleNPJ digital medicine2021
Development and validation of a reinforcement learning algorithm to dynamically optimize mechanical ventilation in critical care.
Article in NPJ digital medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
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Who cites it
55 citing papers in PubMed, 1 synthesis or guideline pooled it, 128 citations in OpenAlex.
- A systematic review of machine learning models for management, prediction and classification of ARDS.Respiratory research · 2024Pooled it
- Development and validation of VentPilot: an AI-based recommendation system for mechanical ventilation.Journal of intensive care · 2026Article
- Observational
- Physiology-guided mechanical ventilation: Monitoring, proportional assist, and bounded automation.World journal of critical care medicine · 2026Review
- Reinforcement Learning for Intraoperative Hypotension Management with Consideration to Postoperative Acute Kidney Injury.Kidney360 · 2026Article
- Computational tools for personalizing treatment of acute respiratory failure, from machine learning to digital twins: a narrative review.Critical care (London, England) · 2026Review
- Off by a beat: the effects of temporal misalignment in reinforcement learning for sepsis treatment.NPJ digital medicine · 2026Article
- Federated Inverse Probability Treatment Weighting for Individual Treatment Effect Estimation.ACM transactions on intelligent systems and technology · 2026Article
- Liver transplant donor-recipient matching with offline reinforcement learning.NPJ digital medicine · 2026Article
- rECMOmender: Reinforcement Learning for Decision Support in Venovenous Extracorporeal Membrane Oxygenation Management.Critical care explorations · 2026Article
- Energy expenditure measurement in critical care: Implications for personalized nutrition support.World journal of critical care medicine · 2025Article
- Current Perspectives on the Artificial Intelligence in Critical Care Medicine.Anesthesiology clinics · 2025Review
- Physicians' Attitudes Toward Artificial Intelligence in Medicine: Mixed Methods Survey and Interview Study.Journal of medical Internet research · 2025Article
- Optimizing Vital Signs in Patients With Traumatic Brain Injury: Reinforcement Learning Algorithm Development and Validation.Journal of medical Internet research · 2025Article
- Can we predict the future of respiratory failure prediction?Critical care (London, England) · 2025Review
- Machine Learning and Artificial Intelligence in Intensive Care Medicine: Critical Recalibrations from Rule-Based Systems to Frontier Models.Journal of clinical medicine · 2025Review
- Learning optimal treatment strategies for intraoperative hypotension using deep reinforcement learning.ArXiv · 2025Article
- A distributional reinforcement learning model for optimal glucose control after cardiac surgery.NPJ digital medicine · 2025Article
- Setting the ventilator with AI support: challenges and perspectives.Intensive care medicine · 2025Article
- A common longitudinal intensive care unit data format (CLIF) for critical illness research.Intensive care medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 5 institutions in 3 countries.
Funding
Abstract
The aim of this work was to develop and evaluate the reinforcement learning algorithm VentAI, which is able to suggest a dynamically optimized mechanical ventilation regime for critically-ill patients. We built, validated and tested its performance on 11,943 events of volume-controlled mechanical ventilation derived from 61,532 distinct ICU admissions and tested it on an independent, secondary dataset (200,859 ICU stays; 25,086 mechanical ventilation events). A patient "data fingerprint" of 44 features was extracted as multidimensional time series in 4-hour time steps. We used a Markov decision process, including a reward system and a Q-learning approach, to find the optimized settings for positive end-expiratory pressure (PEEP), fraction of inspired oxygen (FiO
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.