A Novel Natural Language Processing Tool Improves Colonoscopy Auditing of Adenoma and Serrated Polyp Detection Rates.

Journal: Journal of gastroenterology and hepatology
PMID: 40162890

Abstract

BACKGROUND AND STUDY AIMS: Determining adenoma detection rate (ADR) and serrated polyp detection rate (SDR) can be challenging as they usually involve manual matching of colonoscopy and histology reports. This study aimed to validate a Natural Language Processing (NLP) code that enables rapid and efficient data extraction to calculate ADR and SDR.

Authors

  • Melissa Chew
    Department of Gastroenterology, Northern Health, Melbourne, Victoria, Australia.
  • Catherine Yu
    Department of Gastroenterology, Northern Health, Melbourne, Victoria, Australia.
  • Leanne Stojevski
    Department of Client Data Management, Northern Health, Melbourne, Victoria, Australia.
  • Paul Conilione
    Department of Data Science and Analytics, Northern Health, Melbourne, Victoria, Australia.
  • Anthony Gust
    Department of Data Science and Analytics, Northern Health, Melbourne, Victoria, Australia.
  • Mani Suleiman
    Department of Research, Northern Centre of Health Education and Research, Melbourne, Victoria, Australia.
  • Will Swansson
    Department of Medicine, Northern Health Clinical School, University of Melbourne, Melbourne, Victoria, Australia.
  • Bennett Anderson
    Department of Medicine, Northern Health Clinical School, University of Melbourne, Melbourne, Victoria, Australia.
  • Mayur Garg
    Department of Medicine, The University of Melbourne, Melbourne, Victoria, Australia; Department of Gastroenterology, Northern Health, Epping, Victoria, Australia.
  • Diana Lewis
    Department of Gastroenterology, Northern Health, Melbourne, Victoria, Australia.

Keywords

External Resources

Popular Topics
Recent Journals