Validation of a natural language processing algorithm to identify adenomas and measure adenoma detection rates across a health system: a population-level study.

Journal: Gastrointestinal endoscopy
Published Date: Jan 1, 2023

Abstract

BACKGROUND AND AIMS: Measuring adenoma detection rates (ADRs) at the population level is challenging because pathology reports are often reported in an unstructured format; further, there is significant variation in reporting methods across institutions. Natural language processing (NLP) can be used to extract relevant information from text-based records. We aimed to develop and validate an NLP algorithm to identify colorectal adenomas that could be used to report ADR at the population level in Ontario, Canada.

Authors

  • Jill Tinmouth
    ColonCancerCheck Program, Ontario Health (Cancer Care Ontario), Toronto, Ontario, Canada; Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada; Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada; Division of Gastroenterology, Department of Medicine, University of Toronto, Toronto, Ontario, Canada.
  • Deepak Swain
    ColonCancerCheck Program, Ontario Health (Cancer Care Ontario), Toronto, Ontario, Canada.
  • Katherine Chorneyko
    Laboratory Services, Brantford General Hospital, Brantford, Ontario, Canada.
  • Vicki Lee
    ColonCancerCheck Program, Ontario Health (Cancer Care Ontario), Toronto, Ontario, Canada.
  • Barbara Bowes
    Women's College Hospital, Toronto, Ontario, Canada.
  • Yingzi Li
    ColonCancerCheck Program, Ontario Health (Cancer Care Ontario), Toronto, Ontario, Canada.
  • Julia Gao
    ColonCancerCheck Program, Ontario Health (Cancer Care Ontario), Toronto, Ontario, Canada.
  • David Morgan
    Service of Gastroenterology, St Joseph's Hospital, Hamilton, Ontario, Canada; Division of Gastroenterology, Department of Medicine, McMaster University, Hamilton, Ontario, Canada.

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