Identifying and characterising asthma subgroups at high risk of severe exacerbations using machine learning and longitudinal real-world data.

Journal: BMJ health & care informatics
Published Date: Jul 7, 2025

Abstract

OBJECTIVES: To identify and characterise distinct subgroups of patients with asthma with severe acute exacerbations (AEs) by using a multistep clustering methodology that combines supervised and unsupervised machine learning.

Authors

  • Andres Quintero
    Pfizer, New York, New York, USA ajquintero.ads@gmail.com.
  • Javier Lopez-Molina
    IQVIA, London, UK.
  • Merina Su
    IQVIA, London, UK.
  • Patrick Long
    IQVIA, London, UK.
  • Nicola Boulter
    IQVIA, London, UK.
  • Cindy Weber
    IQVIA, London, UK.
  • Ralica Dimitrova
    Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom; Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.

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