Accurate Identification of Colonoscopy Quality and Polyp Findings Using Natural Language Processing.

Journal: Journal of clinical gastroenterology
Published Date: Jan 1, 2019

Abstract

OBJECTIVES: The aim of this study was to test the ability of a commercially available natural language processing (NLP) tool to accurately extract examination quality-related and large polyp information from colonoscopy reports with varying report formats.

Authors

  • Jeffrey K Lee
    Department of Medicine, Division of Gastroenterology, University of California San Francisco, San Francisco.
  • Christopher D Jensen
    Division of Research, Kaiser Permanente Northern California, Oakland, CA.
  • Theodore R Levin
    Division of Research, Kaiser Permanente Northern California, Oakland, CA.
  • Ann G Zauber
    Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY.
  • Chyke A Doubeni
    Department of Family Medicine, University of Pennsylvania, Philadelphia, PA.
  • Wei K Zhao
    Division of Research, Kaiser Permanente Northern California, Oakland, CA.
  • Douglas A Corley
    Division of Research, Kaiser Permanente Northern California, Oakland, CA.

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