Publication

Machine Learning Methods to Predict Acute Respiratory Failure and Acute Respiratory Distress Syndrome.

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Last modified
  • 05/22/2025
Type of Material
Authors
    An-Kwok Ian Wong, Emory UniversityPatricia C. Cheung, Emory UniversityRishikesan Kamaleswaran, Emory UniversityGregory Martin, Emory UniversityAndre L. Holder, Emory University
Language
  • English
Date
  • 2020
Publisher
  • Frontiers
Publication Version
Copyright Statement
  • © 2020 Wong, Cheung, Kamaleswaran, Martin and Holder.
License
Final Published Version (URL)
Title of Journal or Parent Work
Volume
  • 3
Start Page
  • 579774
End Page
  • 579774
Grant/Funding Information
  • AIW was supported by the NIGMS 2T32GM095442. RK was supported by the Michael J. Fox Foundation (Grant # 17267). ALH was supported by KL2TR002381 and the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number UL1TR002378. GM was supported by grants from the National Center for Advancing Translational Science, the Biomedical Advanced Research and Development Authority, the Marcus Foundation, and the National Institute of Biomedical Imaging and Bioengineering.
Abstract
  • Acute respiratory failure (ARF) is a common problem in medicine that utilizes significant healthcare resources and is associated with high morbidity and mortality. Classification of acute respiratory failure is complicated, and it is often determined by the level of mechanical support that is required, or the discrepancy between oxygen supply and uptake. These phenotypes make acute respiratory failure a continuum of syndromes, rather than one homogenous disease process. Early recognition of the risk factors for new or worsening acute respiratory failure may prevent that process from occurring. Predictive analytical methods using machine learning leverage clinical data to provide an early warning for impending acute respiratory failure or its sequelae. The aims of this review are to summarize the current literature on ARF prediction, to describe accepted procedures and common machine learning tools for predictive tasks through the lens of ARF prediction, and to demonstrate the challenges and potential solutions for ARF prediction that can improve patient outcomes.
Author Notes
Keywords
Research Categories
  • Health Sciences, Medicine and Surgery
  • Health Sciences, Health Care Management

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