Automating the Capture of Structured Pathology Data for Prostate Cancer Clinical Care and Research.

Journal: JCO clinical cancer informatics
Published Date: Jul 1, 2019

Abstract

PURPOSE: Cancer pathology findings are critical for many aspects of care but are often locked away as unstructured free text. Our objective was to develop a natural language processing (NLP) system to extract prostate pathology details from postoperative pathology reports and a parallel structured data entry process for use by urologists during routine documentation care and compare accuracy when compared with manual abstraction and concordance between NLP and clinician-entered approaches.

Authors

  • Anobel Y Odisho
    University of California, San Francisco, San Francisco, CA.
  • Mark Bridge
    University of California, San Francisco, San Francisco, CA.
  • Mitchell Webb
    University of California, San Francisco Medical Center, San Francisco, CA.
  • Niloufar Ameli
    University of California, San Francisco, San Francisco, CA.
  • Renu S Eapen
    University of California, San Francisco, San Francisco, CA.
  • Frank Stauf
    University of California, San Francisco, San Francisco, CA.
  • Janet E Cowan
    University of California, San Francisco, San Francisco, CA.
  • Samuel L Washington
    University of California, San Francisco, San Francisco, CA.
  • Annika Herlemann
    University of California, San Francisco, San Francisco, CA.
  • Peter R Carroll
    University of California, San Francisco, San Francisco, CA.
  • Matthew R Cooperberg
    University of California, San Francisco, San Francisco, CA.

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