Automated derivation of diagnostic criteria for lung cancer using natural language processing on electronic health records: a pilot study.

Journal: BMC medical informatics and decision making
PMID: 39633397

Abstract

BACKGROUND: The digitisation of healthcare records has generated vast amounts of unstructured data, presenting opportunities for improvements in disease diagnosis when clinical coding falls short, such as in the recording of patient symptoms. This study presents an approach using natural language processing to extract clinical concepts from free-text which are used to automatically form diagnostic criteria for lung cancer from unstructured secondary-care data.

Authors

  • Andrew Houston
    School of Computer Science, Loughborough University, Loughborough, LE11 3TU, UK. A.Houston@lboro.ac.uk.
  • Sophie Williams
    King's Institute of Therapeutic Endoscopy, King's College Hospital NHS Foundation Trust, London.
  • William Ricketts
    Respiratory Medicine, Barts Health NHS Trust, London, UK.
  • Charles Gutteridge
    Barts Life Sciences, Barts Health NHS Trust, London, UK.
  • Chris Tackaberry
    Clinithink Ltd., London, UK.
  • John Conibear
    Department of Clinical Oncology, St Batholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK.

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