Factors Associated with Abusive Head Trauma in Young Children Presenting to Emergency Medical Services Using a Large Language Model.

Journal: Prehospital emergency care
PMID: 39803993

Abstract

OBJECTIVES: Abusive head trauma (AHT) is a leading cause of death in young children. Analyses of patient characteristics presenting to Emergency Medical Services (EMS) are often limited to structured data fields. Artificial Intelligence (AI) and Large Language Models (LLM) may identify rare presentations like AHT through factors not found in structured data. Our goal was to apply AI and LLM to EMS narrative documentation of young children to detect AHT.

Authors

  • Allison Broad
    University of Colorado School of Medicine, Aurora, Colorado.
  • Xiao Luo
    Department of Spine Surgery, The Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang, China.
  • Fattah Muhammad Tahabi
    Department of Management Science & Information Systems, Oklahoma State University, Stillwater, Oklahoma.
  • Denise Abdoo
    Department of Pediatrics, University of Colorado School of Medicine, Aurora, Colorado.
  • Zhan Zhang
    School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.
  • Kathleen Adelgais
    Department of Pediatrics, University of Colorado School of Medicine, Aurora, Colorado.

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