Identification of Gout Flares in Chief Complaint Text Using Natural Language Processing.

Journal: AMIA ... Annual Symposium proceedings. AMIA Symposium
PMID: 33936473

Abstract

Many patients with gout flares treated in the Emergency Department (ED) often do not receive optimal continuity of care after an ED visit. Thus, developing methods to identify patients with gout flares in the ED and referring them to appropriate outpatient gout care is required. While Natural Language Processing (NLP) has been used to detect gout flares retrospectively, it is much more challenging to identify patients prospectively during an ED visit where documentation is usually minimal. We annotate a corpus of ED triage nurse chief complaint notes for the presence of gout flares and implement a simple algorithm for gout flare ED alerts. We show that the chief complaint alone has strong predictive power for gout flares. We make available a de-identified version of this corpus annotated for gout mentions, which is to our knowledge the first free text chief complaint clinical corpus available.

Authors

  • John D Osborne
    Center for Clinical and Translational Science, University of Alabama at Birmingham, Birmingham, Alabama, USA, 35294 ozborn@uab.edu.
  • James S Booth
    University of Alabama at Birmingham, Birmingham, Alabama, USA.
  • Tobias O'Leary
    University of Alabama at Birmingham, Birmingham, Alabama, USA.
  • Amy Mudano
    University of Alabama at Birmingham, Birmingham, Alabama, USA.
  • Giovanna Rosas
    University of Alabama at Birmingham, Birmingham, Alabama, USA.
  • Phillip J Foster
    University of Alabama at Birmingham, Birmingham, Alabama, USA.
  • Kenneth G Saag
    University of Alabama at Birmingham, Birmingham, Alabama, USA.
  • Maria I Danila
    University of Alabama at Birmingham, 7th Ave S, Birmingham, 1720, USA.

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