Predicting Extubation Readiness in Preterm Infants Utilizing Machine Learning: A Diagnostic Utility Study.

Journal: The Journal of pediatrics
PMID: 38561049

Abstract

OBJECTIVE: The objective of this study was to predict extubation readiness in preterm infants using machine learning analysis of bedside pulse oximeter and ventilator data.

Authors

  • Mandy Brasher
    Department of Pediatrics/Neonatology, College of Medicine, University of Kentucky, Lexington, KY.
  • Alexandr Virodov
    Institute of Biomedical Informatics, University of Kentucky, Lexington, KY.
  • Thomas M Raffay
    Department of Pediatrics/Neonatology, College of Medicine, Case Western Reserve University, Cleveland, OH.
  • Henrietta S Bada
    Department of Pediatrics/Neonatology, College of Medicine, University of Kentucky, Lexington, KY.
  • M Douglas Cunningham
    Department of Pediatrics/Neonatology, College of Medicine, University of Kentucky, Lexington, KY.
  • Cody Bumgardner
    Institute of Biomedical Informatics, University of Kentucky, Lexington, KY.
  • Elie G Abu Jawdeh
    Department of Pediatrics/Neonatology, College of Medicine, University of Kentucky, Lexington, KY. Electronic address: elie.abujawdeh@uky.edu.

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