Integrating structured and unstructured data for predicting emergency severity: an association and predictive study using transformer-based natural language processing models.

Journal: BMC medical informatics and decision making
Published Date: Dec 5, 2024

Abstract

BACKGROUND: Efficient triage in emergency departments (EDs) is critical for timely and appropriate care. Traditional triage systems primarily rely on structured data, but the increasing availability of unstructured data, such as clinical notes, presents an opportunity to enhance predictive models for assessing emergency severity and to explore associations between patient characteristics and severity outcomes. This study aimed to evaluate the effectiveness of combining structured and unstructured data to predict emergency severity more accurately.

Authors

  • Xingyu Zhang
    Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA, USA.
  • Yanshan Wang
    Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
  • Yun Jiang
  • Charissa B Pacella
    Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
  • Wenbin Zhang
    Department of Epidemiology and Medical Statistics School of Public Health, Guangdong Medical University, Dongguan, Guangdong, China.

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