Care home resident identification: A comparison of address matching methods with Natural Language Processing.

Journal: PloS one
PMID: 39637177

Abstract

BACKGROUND: Care home residents are a highly vulnerable group, but identifying care home residents in routine data is challenging. This study aimed to develop and validate Natural Language Processing (NLP) methods to identify care home residents from primary care address records.

Authors

  • Víctor Suárez-Paniagua
    Department of Computer Science, University Carlos III of Madrid Leganés 28911, Madrid, Spain.
  • Arlene Casey
    School of Literatures, Languages and Cultures (LLC), University of Edinburgh, Edinburgh, Scotland, UK.
  • Charis A Marwick
    Population Health & Genomics Division, School of Medicine, University of Dundee, Dundee, United Kingdom.
  • Jennifer K Burton
    School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, United Kingdom.
  • Helen Callaby
    NHS Grampian, Aberdeen, United Kingdom.
  • Isobel Guthrie
    University of St Andrews School of Biology, Biomedical Sciences Research Complex, St Andrews, United Kingdom.
  • Bruce Guthrie
    Usher Institute of Population Health Sciences and Informatics, Advanced Care Research Centre, University of Edinburgh, Edinburgh, United Kingdom.
  • Beatrice Alex
    The Alan Turing Institute, British Library, 96 Euston Road, London, UK.

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