Developing a short-term prediction model for asthma exacerbations from Swedish primary care patients' data using machine learning - Based on the ARCTIC study.

Journal: Respiratory medicine
PMID: 34077873

Abstract

OBJECTIVE: The ability to predict impending asthma exacerbations may allow better utilization of healthcare resources, prevention of hospitalization and improve patient outcomes. We aimed to develop models using machine learning to predict risk of exacerbations.

Authors

  • Karin Lisspers
    Department of Public Health and Caring Sciences, Family Medicine and Preventive Medicine, Uppsala University, Uppsala, Sweden. Electronic address: karin.lisspers@regiondalarna.se.
  • Björn Ställberg
    Department of Public Health and Caring Sciences, Family Medicine and Preventive Medicine, Uppsala University, Uppsala, Sweden.
  • Kjell Larsson
    Integrative Toxicology, Karolinska Institutet, Stockholm, Sweden.
  • Christer Janson
    Department of Medical Sciences: Respiratory, Allergy and Sleep Research, Uppsala University, Uppsala, Sweden.
  • Mario Müller
    IQVIA, Frankfurt am Main, Germany.
  • Mateusz Łuczko
    IQVIA, Warsaw, Poland.
  • Bine Kjøller Bjerregaard
    IQVIA, Copenhagen, Denmark.
  • Gerald Bacher
    Novartis Pharma AG, Basel, Switzerland.
  • Björn Holzhauer
    Novartis Pharma AG, Basel, Switzerland.
  • Pankaj Goyal
    Novartis Pharma AG, Basel, Switzerland.
  • Gunnar Johansson
    Department of Public Health and Caring Sciences, Family Medicine and Preventive Medicine, Uppsala University, Uppsala, Sweden.

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