Predicting type 1 diabetes in children using electronic health records in primary care in the UK: development and validation of a machine-learning algorithm.

Journal: The Lancet. Digital health
PMID: 38789139

Abstract

BACKGROUND: Children presenting to primary care with suspected type 1 diabetes should be referred immediately to secondary care to avoid life-threatening diabetic ketoacidosis. However, early recognition of children with type 1 diabetes is challenging. Children might not present with classic symptoms, or symptoms might be attributed to more common conditions. A quarter of children present with diabetic ketoacidosis, a proportion unchanged over 25 years. Our aim was to investigate whether a machine-learning algorithm could lead to earlier detection of type 1 diabetes in primary care.

Authors

  • Rhian Daniel
    Division of Population Medicine, School of Medicine, Cardiff University, Cardiff, UK.
  • Hywel Jones
    Division of Population Medicine, School of Medicine, Cardiff University, Cardiff, UK.
  • John W Gregory
    Division of Population Medicine, School of Medicine, Cardiff University, Cardiff, UK.
  • Ambika Shetty
    The Noah's Ark Children's Hospital for Wales, Department of Paediatric Diabetes and Endocrinology, Cardiff and Vale University Health Board, Cardiff, UK.
  • Nick Francis
    Primary Care Research Centre, University of Southampton, Southampton, UK.
  • Shantini Paranjothy
    School of Medicine, Institute of Applied Health Sciences, Aberdeen, UK.
  • Julia Townson
    Centre for Trials Research, Cardiff University, Cardiff, UK. Electronic address: townson@cardiff.ac.uk.

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