Using natural language processing in emergency medicine health service research: A systematic review and meta-analysis.

Journal: Academic emergency medicine : official journal of the Society for Academic Emergency Medicine
PMID: 38757352

Abstract

OBJECTIVES: Natural language processing (NLP) represents one of the adjunct technologies within artificial intelligence and machine learning, creating structure out of unstructured data. This study aims to assess the performance of employing NLP to identify and categorize unstructured data within the emergency medicine (EM) setting.

Authors

  • Hao Wang
    Department of Cardiology, Second Medical Center, Chinese PLA General Hospital, Beijing, China.
  • Naomi Alanis
    Department of Emergency Medicine, JPS Health Network, Fort Worth, Texas, USA.
  • Laura Haygood
    Health Sciences Librarian for Public Health, Brown University, Providence, Rhode Island, USA.
  • Thomas K Swoboda
    Department of Emergency Medicine, The Valley Health System, Touro University Nevada School of Osteopathic Medicine, Las Vegas, Nevada, USA.
  • Nathan Hoot
    Department of Emergency Medicine, JPS Health Network, Fort Worth, Texas, USA.
  • Daniel Phillips
    East of England Ambulance Service NHS Trust, Cambridgeshire, UK.
  • Heidi Knowles
    Department of Emergency Medicine, JPS Health Network, Fort Worth, Texas, USA.
  • Sara Ann Stinson
    Mary Couts Burnett Library, Burnett School of Medicine at Texas Christian University, Fort Worth, Texas, USA.
  • Prachi Mehta
    Department of Emergency Medicine, JPS Health Network, Fort Worth, Texas, USA.
  • Usha Sambamoorthi
    Department of Pharmacotherapy, College of Pharmacy, "Vashisht" Professor of Disparities, Health Education, Awareness & Research in Disparities (HEARD) Scholar, Texas Center for Health Disparities, University of North Texas Health Sciences Center, 3500 Camp Bowie Blvd, Fort Worth, TX, 76107, USA. Electronic address: usha.sambamoorthi@unthsc.edu.

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