Skip to main content
 

Main menu

  • Home
  • Content
    • Current Issue
    • Editor's Commentary
    • Archives
    • Most-Read Papers of 2022
  • Authors
    • Author Guidelines
    • Submit a Manuscript
  • Reviewers
    • Reviewer Information
    • Create Reviewer Account
    • Reviewer Guidelines: Original Research
    • Reviewer Guidelines: Reviews
    • Appreciation of Reviewers
  • CRCE
    • Through the Journal
    • JournalCasts
    • AARC University
    • PowerPoint Template
  • Open Forum
    • 2023 Call for Abstracts
    • 2022 Abstracts
    • Previous Open Forums
  • Podcast
    • English
    • Español
    • Portugûes
    • 国语
  • Videos
    • Video Abstracts
    • Author Interviews
    • Highlighted Articles
    • The Journal

User menu

  • Subscribe
  • My alerts
  • Log in

Search

  • Advanced search
American Association for Respiratory Care
  • Subscribe
  • My alerts
  • Log in
American Association for Respiratory Care

Advanced Search

  • Home
  • Content
    • Current Issue
    • Editor's Commentary
    • Archives
    • Most-Read Papers of 2022
  • Authors
    • Author Guidelines
    • Submit a Manuscript
  • Reviewers
    • Reviewer Information
    • Create Reviewer Account
    • Reviewer Guidelines: Original Research
    • Reviewer Guidelines: Reviews
    • Appreciation of Reviewers
  • CRCE
    • Through the Journal
    • JournalCasts
    • AARC University
    • PowerPoint Template
  • Open Forum
    • 2023 Call for Abstracts
    • 2022 Abstracts
    • Previous Open Forums
  • Podcast
    • English
    • Español
    • Portugûes
    • 国语
  • Videos
    • Video Abstracts
    • Author Interviews
    • Highlighted Articles
    • The Journal
  • Twitter
  • Facebook
  • YouTube
Research ArticleOriginal Research

Predicting Failure of Noninvasive Respiratory Support Using Deep Recurrent Learning

Patrick T Essay, Jarrod M Mosier, Amin Nayebi, Julia M Fisher and Vignesh Subbian
Respiratory Care February 2023, respcare.10382; DOI: https://doi.org/10.4187/respcare.10382
Patrick T Essay
Department of Systems and Industrial Engineering, College of Engineering, The University of Arizona, Tucson, Arizona.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jarrod M Mosier
Department of Emergency Medicine, The University of Arizona College of Medicine, Tucson, Arizona; and Division of Pulmonary, Allergy, Critical Care, and Sleep, Department of Medicine, The University of Arizona College of Medicine, Tucson, Arizona.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: [email protected]
Amin Nayebi
Department of Systems and Industrial Engineering, College of Engineering, The University of Arizona, Tucson, Arizona.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Julia M Fisher
Statistics Consulting Laboratory, BIO5 Institute, The University of Arizona, Tucson, Arizona.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Vignesh Subbian
Department of Systems and Industrial Engineering, College of Engineering, The University of Arizona, Tucson, Arizona; Department of Biomedical Engineering, College of Engineering, The University of Arizona, Tucson, Arizona; and BIO5 Institute, The University of Arizona, Tucson, Arizona.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Article
  • Figures & Data
  • Supplemental
  • Info & Metrics
  • References
  • PDF
Loading

Article Information

respcare.10382
DOI 
https://doi.org/10.4187/respcare.10382
PubMed 
36543341

Published By 
Respiratory Care
Print ISSN 
0020-1324
Online ISSN 
1943-3654
History 
  • Published online February 21, 2023.

Article Versions

  • Latest version (December 21, 2022 - 05:18).
  • You are currently viewing a Latest version of this article (February 21, 2023 - 09:22).
  • View the most recent version of this article
Copyright & Usage 
Copyright © 2023 by Daedalus Enterprises

Author Information

  1. Patrick T Essay,
  2. Jarrod M Mosier⇑,
  3. Amin Nayebi,
  4. Julia M Fisher and
  5. Vignesh Subbian
  1. Department of Systems and Industrial Engineering, College of Engineering, The University of Arizona, Tucson, Arizona.
  2. Department of Emergency Medicine, The University of Arizona College of Medicine, Tucson, Arizona; and Division of Pulmonary, Allergy, Critical Care, and Sleep, Department of Medicine, The University of Arizona College of Medicine, Tucson, Arizona.
  3. Statistics Consulting Laboratory, BIO5 Institute, The University of Arizona, Tucson, Arizona.
  4. Department of Systems and Industrial Engineering, College of Engineering, The University of Arizona, Tucson, Arizona; Department of Biomedical Engineering, College of Engineering, The University of Arizona, Tucson, Arizona; and BIO5 Institute, The University of Arizona, Tucson, Arizona.

PreviousNext
Back to top

In this issue

Respiratory Care: 68 (6)
Respiratory Care
Vol. 68, Issue 6
1 Jun 2023
  • Table of Contents
  • Table of Contents (PDF)
  • Cover (PDF)
  • Index by author

 

Download PDF
Article Alerts
Sign In to Email Alerts with your Email Address
Email Article

Thank you for your interest in spreading the word on American Association for Respiratory Care.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
Predicting Failure of Noninvasive Respiratory Support Using Deep Recurrent Learning
(Your Name) has sent you a message from American Association for Respiratory Care
(Your Name) thought you would like to see the American Association for Respiratory Care web site.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Citation Tools
Predicting Failure of Noninvasive Respiratory Support Using Deep Recurrent Learning
Patrick T Essay, Jarrod M Mosier, Amin Nayebi, Julia M Fisher, Vignesh Subbian
Respiratory Care Feb 2023, respcare.10382; DOI: 10.4187/respcare.10382

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero

Share
Predicting Failure of Noninvasive Respiratory Support Using Deep Recurrent Learning
Patrick T Essay, Jarrod M Mosier, Amin Nayebi, Julia M Fisher, Vignesh Subbian
Respiratory Care Feb 2023, respcare.10382; DOI: 10.4187/respcare.10382
del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Jump to section

  • Article
  • Figures & Data
  • Supplemental
  • Info & Metrics
  • References
  • PDF

Related Articles

Cited By...

Keywords

  • machine learning
  • deep neural network
  • ICU
  • Respiratory Failure
  • mechanical ventilation

Info For

  • Subscribers
  • Institutions
  • Advertisers

About Us

  • About the Journal
  • Editorial Board

AARC

  • Membership
  • Meetings
  • Clinical Practice Guidelines

More

  • Contact Us
  • RSS
American Association for Respiratory Care

Print ISSN: 0020-1324        Online ISSN: 1943-3654

© Daedalus Enterprises, Inc.

Powered by HighWire