Efficient identification of nationally mandated reportable cancer cases using natural language processing and machine learning.

Journal: Journal of the American Medical Informatics Association : JAMIA
PMID:

Abstract

OBJECTIVE: To help cancer registrars efficiently and accurately identify reportable cancer cases.

Authors

  • John D Osborne
    Center for Clinical and Translational Science, University of Alabama at Birmingham, Birmingham, Alabama, USA, 35294 ozborn@uab.edu.
  • Matthew Wyatt
    Center for Clinical and Translational Science, University of Alabama at Birmingham, Birmingham, Alabama, USA, 35294.
  • Andrew O Westfall
    Department of Biostatistics, University of Alabama at Birmingham, Birmingham, Alabama, USA, 35294.
  • James Willig
    Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA, 35294.
  • Steven Bethard
    Department of Computer and Information Science, University of Alabama at Birmingham, Birmingham, Alabama, USA, 35294.
  • Geoff Gordon
    Informatics Institute, University of Alabama at Birmingham, Birmingham, Alabama, USA, 35294.