Identification of severe acute pediatric asthma phenotypes using unsupervised machine learning.

Journal: Pediatric pulmonology
PMID:

Abstract

RATIONALE: More targeted management of severe acute pediatric asthma could improve clinical outcomes.

Authors

  • Colin Rogerson
    Division of Critical Care, Department of Pediatrics, Indiana University, Indianapolis, IN.
  • L Nelson Sanchez-Pinto
    Anne & Robert H. Lurie Children's Hospital of Chicago, Northwestern University, Chicago, Illinois, USA.
  • Benjamin Gaston
    Case Western Reserve University, Cleveland, OH, USA. begaston@iu.edu.
  • Sarah Wiehe
    Department of Pediatrics, Indiana University School of Medicine, Indianapolis, Indiana, USA.
  • Titus Schleyer
    Center for Biomedical Informatics, Regenstrief institute, Inc., Indianapolis, IN, USA.
  • Wanzhu Tu
    Department of Biostatistics and Health Data Science, Indiana University School of Medicine.
  • Eneida Mendonça
    Department of Pediatrics, College of Medicine, University of Cincinnati, Cincinnati, OH, United States.