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

John D Osborne1, James S Booth1, Tobias O'Leary1

  • 1University of Alabama at Birmingham, Birmingham, Alabama, USA.

AMIA ... Annual Symposium Proceedings. AMIA Symposium
|May 3, 2021
PubMed

Insights

Identifying gout flares in the Emergency Department (ED) is crucial for patient care. A simple algorithm using chief complaints can effectively flag gout flares, improving outpatient referrals and continuity of care.

Area of Science:

  • Medical Informatics
  • Clinical Research
  • Rheumatology

Background:

  • Patients with gout flares treated in the Emergency Department (ED) often lack continuity of care post-visit.
  • Effective identification and referral to outpatient gout management are needed.
  • Prospective identification of gout flares in the ED is challenging due to minimal documentation.

Purpose of the Study:

  • To develop and evaluate a method for prospectively identifying gout flares in ED patients.
  • To assess the predictive power of chief complaint notes for gout flares.
  • To create and share a valuable clinical corpus for Natural Language Processing (NLP) research.

Main Methods:

  • Annotation of ED triage nurse chief complaint notes for gout flares.
  • Implementation of a simple algorithm for generating gout flare ED alerts.
  • Creation of a de-identified corpus of free text chief complaint notes.

Main Results:

  • Chief complaint notes alone demonstrate strong predictive power for identifying gout flares.
  • A functional algorithm for ED gout flare alerts was developed.
  • A novel, de-identified clinical corpus of chief complaints was made publicly available.

Conclusions:

  • Chief complaints in ED triage notes are highly effective for prospective gout flare identification.
  • This approach can facilitate timely referrals and improve continuity of care for gout patients.
  • The released corpus supports further NLP research in clinical settings.

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