Determining post-test risk in a national sample of stress nuclear myocardial perfusion imaging reports: Implications for natural language processing tools.

Journal: Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
PMID: 29696484

Abstract

BACKGROUND: Reporting standards promote clarity and consistency of stress myocardial perfusion imaging (MPI) reports, but do not require an assessment of post-test risk. Natural Language Processing (NLP) tools could potentially help estimate this risk, yet it is unknown whether reports contain adequate descriptive data to use NLP.

Authors

  • Andrew E Levy
    Division of Cardiology, Department of Medicine, University of Colorado School of Medicine, Aurora, CO, USA. Andrew.levy@ucdenver.edu.
  • Nishant R Shah
    Division of Cardiology, Department of Medicine, Brown University Alpert Medical School, Providence, RI, USA.
  • Michael E Matheny
    Vanderbilt University School of Medicine, Nashville, TN.
  • Ruth M Reeves
    Health Services Research & Development, VA Tennessee Valley Healthcare System, Nashville, TN, USA.
  • Glenn T Gobbel
    Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA.
  • Steven M Bradley
    Cardiovascular Medicine, VA Eastern Colorado Healthcare System, Denver, CO, USA.

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