Predicting asthma attacks in primary care: protocol for developing a machine learning-based prediction model.

Journal: BMJ open
PMID: 31292179

Abstract

INTRODUCTION: Asthma is a long-term condition with rapid onset worsening of symptoms ('attacks') which can be unpredictable and may prove fatal. Models predicting asthma attacks require high sensitivity to minimise mortality risk, and high specificity to avoid unnecessary prescribing of preventative medications that carry an associated risk of adverse events. We aim to create a risk score to predict asthma attacks in primary care using a statistical learning approach trained on routinely collected electronic health record data.

Authors

  • Holly Tibble
    Usher Institute of Population Health Sciences and Informatics, Edinburgh Medical School, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, UK.
  • Athanasios Tsanas
    Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, UK.
  • Elsie Horne
    Usher Institute of Population Health Sciences and Informatics, Edinburgh Medical School, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, UK.
  • Robert Horne
    Asthma UK Centre for Applied Research, Edinburgh, UK.
  • Mehrdad Mizani
    Usher Institute of Population Health Sciences and Informatics, Edinburgh Medical School, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, UK.
  • Colin R Simpson
    Asthma UK Centre for Applied Research, Edinburgh, UK.
  • Aziz Sheikh
    Asthma UK Center for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom.

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