Predicting Failure of Noninvasive Respiratory Support Using Deep Recurrent Learning

Patrick T Essay; Jarrod M Mosier; Amin Nayebi; Julia M Fisher; Vignesh Subbian

doi:10.4187/respcare.10382

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.

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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.

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For correspondence: [email protected]

Amin Nayebi

Department of Systems and Industrial Engineering, College of Engineering, The University of Arizona, Tucson, Arizona.

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Julia M Fisher

Statistics Consulting Laboratory, BIO5 Institute, The University of Arizona, Tucson, Arizona.

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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.

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Respiratory Care

Vol. 68, Issue 6

1 Jun 2023

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Predicting Failure of Noninvasive Respiratory Support Using Deep Recurrent Learning