Analyzing pain patterns in the emergency department: Leveraging clinical text deep learning models for real-world insights.

Journal: International journal of medical informatics
PMID: 39003790

Abstract

OBJECTIVE: To determine the incidence of patients presenting in pain to a large Australian inner-city emergency department (ED) using a clinical text deep learning algorithm.

Authors

  • James A Hughes
    School of Nursing, Centre for Healthcare Transformation, Queensland University of Technology, Brisbane, Australia.
  • Yutong Wu
    The Australian e-Health Research Centre, CSIRO, Brisbane, Australia.
  • Lee Jones
    School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia.
  • Clint Douglas
    School of Nursing, Centre for Healthcare Transformation, Queensland University of Technology, Brisbane, Australia.
  • Nathan Brown
    BenevolentAI, 40 Churchway, London, NW1 1LW, UK.
  • Sarah Hazelwood
    Emergency Department, The Prince Charles Hospital, Brisbane, Australia.
  • Anna-Lisa Lyrstedt
    Emergency and Trauma Centre, Royal Brisbane and Women's Hospital, Brisbane, Australia.
  • Rajeev Jarugula
    Emergency Department, The Prince Charles Hospital, Brisbane, Australia.
  • Kevin Chu
    Royal Brisbane andWomens Hospital, Brisbane, QLD, Australia.
  • Anthony Nguyen
    Australian e-Health Research Centre, CSIRO, Brisbane, QLD, Australia.

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