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

Clinical Information and Decision Making (1436) Intensive Care Unit (ICU) (114) Machine Learning (1504) Artificial Intelligence (1427) Respiratory Medicine (91) Tools, Programs and Algorithms (275) Clinical Informatics (1050)

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 Tweetations Metrics

Siqi Liu   1 * , PhD ;   Qianyi Xu   2 * , BEng ;   Zhuoyang Xu   3 , MSc ;   Zhuo Liu   3 , MSc ;   Xingzhi Sun   3 , PhD ;   Guotong Xie   3 , PhD ;   Mengling Feng   2, 4 , PhD ;   Kay Choong See   5 , MBBS

1 National University of Singapore Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore, Singapore

2 Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore

3 Ping An Healthcare Technology, Beijing, China

4 Institute of Data Science, National University of Singapore, Singapore, Singapore

5 Division of Respiratory and Critical Care Medicine, Department of Medicine, National University Hospital, Singapore, Singapore

*these authors contributed equally

Corresponding Author:

  • Mengling Feng, PhD
  • Saw Swee Hock School of Public Health
  • National University of Singapore
  • 12 Science Drive 2
  • Singapore, 117549
  • Singapore
  • Phone: 65 65164984
  • Email: ephfm@nus.edu.sg