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A Transparent and Adaptable Method to Extract Colonoscopy and Pathology Data Using Natural Language Processing.

Helene B Fevrier1, Liyan Liu1, Lisa J Herrinton2

  • 1Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA.

Journal of Medical Systems
|August 2, 2020
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Summary
This summary is machine-generated.

A new natural language processing (NLP) tool reliably extracts key colonoscopy and pathology data from reports. This adaptable NLP tool demonstrates high accuracy, aiding clinical research and data analysis.

Keywords:
ColonoscopyNatural language processingPathology report

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Area of Science:

  • Medical Informatics
  • Gastroenterology
  • Natural Language Processing

Background:

  • Extracting key variables from colonoscopy and pathology reports is crucial for clinical research.
  • Existing natural language processing (NLP) tools often lack adaptability to new healthcare settings.
  • Standardized and reliable data extraction methods are needed for large-scale analysis.

Purpose of the Study:

  • To develop a reliable and broadly adaptable natural language processing (NLP) tool for extracting specific colonoscopy and pathology variables.
  • To assess the performance of the developed NLP tool using a validation set.

Main Methods:

  • Utilized SAS® PERL regular expressions to develop an NLP tool on a training set of 1000 colonoscopy and pathology reports (1996-2016).
  • The NLP tool was designed to capture five key variables: polyp type, size, location, procedure extent, and bowel preparation quality.
  • Validated the NLP tool's performance on a separate set of 3000 reports, comparing its classifications against manual chart review using sensitivity, specificity, PPV, and NPV.

Main Results:

  • The NLP tool achieved high performance, with Cohen's κ ranging from 93% to 99%.
  • Sensitivity and specificity were consistently high (95%-100%) across categories.
  • Positive predictive values (PPVs) ranged from 62% to 100%, and negative predictive values (NPVs) ranged from 73% to 100%, demonstrating strong accuracy for most variables.

Conclusions:

  • Developed a transparent and adaptable NLP tool for extracting five essential colonoscopy and pathology variables from clinical reports.
  • The tool demonstrated high accuracy and reliability in a large, community-based population.
  • This NLP tool is readily testable and potentially implementable in diverse healthcare settings for improved data extraction.