This paper is in the following e-collection/theme issue:

Clinical Information and Decision Making (1915) Intensive Care Unit (ICU) (160) Machine Learning (2076) Artificial Intelligence (2129) Respiratory Medicine (157) Tools, Programs and Algorithms (376) Clinical Informatics (1325)

Published on in Vol 26 (2024)

This is a member publication of National University of Singapore

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/44494, first published .
Reinforcement Learning to Optimize Ventilator Settings for Patients on Invasive Mechanical Ventilation: Retrospective Study

Reinforcement Learning to Optimize Ventilator Settings for Patients on Invasive Mechanical Ventilation: Retrospective Study

Reinforcement Learning to Optimize Ventilator Settings for Patients on Invasive Mechanical Ventilation: Retrospective Study

Authors of this article:

Siqi Liu1 Author Orcid Image ;   Qianyi Xu2 Author Orcid Image ;   Zhuoyang Xu3 Author Orcid Image ;   Zhuo Liu3 Author Orcid Image ;   Xingzhi Sun3 Author Orcid Image ;   Guotong Xie3 Author Orcid Image ;   Mengling Feng2, 4 Author Orcid Image ;   Kay Choong See5 Author Orcid Image
Article Authors Cited by (12) Tweetations Metrics

Journals

  1. Fritsch S, Cecconi M. Setting the ventilator with AI support: challenges and perspectives. Intensive Care Medicine 2025;51(3):593 View
  2. Ellertson M, Sharp R. Challenges in Pursuing AI Transparency. The American Journal of Bioethics 2025;25(3):4 View
  3. K B, Venkatesan L, Benjamin L, K V, Satchi N. Reinforcement Learning in Personalized Medicine: A Comprehensive Review of Treatment Optimization Strategies. Cureus 2025 View
  4. Yang H, Hao A, Liu S, Chang Y, Tsai Y, Weng S, Chan M, Wang C, Xu Y. Prediction of Spontaneous Breathing Trial Outcome in Critically Ill-Ventilated Patients Using Deep Learning: Development and Verification Study. JMIR Medical Informatics 2025;13:e64592 View
  5. Zhu J, Wang Y, Wang S, Zhou J. Development and validation of a BMI stratified mortality prediction model for patients with COPD complicated by HF using the MIMIC-IV database. Scientific Reports 2025;15(1) View
  6. Lee J, Yoon J. Current Perspectives on the Artificial Intelligence in Critical Care Medicine. Anesthesiology Clinics 2025;43(3):507 View
  7. Zhang T, He Y. Limitations of large language models in dynamic clinical decision making. Journal of Clinical Anesthesia 2025;106:111942 View
  8. Gupta P, Pearce A, Pham T, Miller M, Brunetti K, Heskett K, Malhotra A, Mayampurath A, Afshar M. Artificial intelligence-driven decision support for patients with acute respiratory failure: a scoping review. Intensive Care Medicine Experimental 2025;13(1) View
  9. Páleník J, Soták M, Černý M, Komarc M, Svoboda N, Hayu D, Tyll T, Netuka D, Masopust V, Roubík K, Májovský M. Conversational AI in tactical combat casualty care: Baseline GPT-4o improves medic decision-making. Clinical Simulation in Nursing 2025;107:101803 View
  10. Shu X, Zhu Y, Liu X, Li Y, Yi B, Wang Y. Applications of artificial intelligence in anesthesiology. Anesthesiology and Perioperative Science 2025;3(4) View
  11. Muñoz J, Fernández-Araujo N, Ruíz-Cacho R, Muñoz-Visedo J. Artificial intelligence in ARDS: From automated support to personalized ventilation. Journal of Intensive Medicine 2025 View

Books/Policy Documents

  1. Quinn C. Generative AI for the Medical Student. View