Detection and prediction of real-world severe asthma phenotypes by application of machine learning to electronic health records.

Journal: The journal of allergy and clinical immunology. Global
Published Date: Apr 17, 2025

Abstract

BACKGROUND: Asthma is a heterogeneous disease with a diverse array of phenotypes that differ in inflammatory characteristics and severity. Identifying and classifying phenotypes in the real world could provide a foundation to improve and personalize asthma management. Leveraging machine learning in analyzing electronic health records (EHRs) provides an opportunity to identify real-world asthma phenotypes.

Authors

  • Mehmet Furkan Bağcı
    Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, Calif.
  • Toan Do
    Department of Allergy & Immunology, University of California San Diego School of Medicine, San Diego, Calif.
  • Samantha R Spierling Bagsic
    Department of Research Development, Scripps Health, San Diego, Calif.
  • Rahul F Gomez
    Department of Knowledge Management, Scripps Health, San Diego, Calif.
  • Judy H Jun
    Department of Knowledge Management, Scripps Health, San Diego, Calif.
  • Anna L Ritko
    Dept. of Knowledge Management, Scripps Health, CA.
  • Sally E Wenzel
    Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Department of Environmental Medicine and Occupational Health, Graduate School of Public Health, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
  • Truong Nguyen
    Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA.
  • Yusuf Ozturk
    San Diego State University, ECE Dept., San Diego, CA 92182.
  • Brian D Modena
    University of California San Diego, ECE Dept., La Jolla, CA 92093.

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