Identification of Gender Differences in Acute Myocardial Infarction Presentation and Management at Aga Khan University Hospital-Pakistan: Natural Language Processing Application in a Dataset of Patients With Cardiovascular Disease.

Journal: JMIR formative research
PMID: 39705071

Abstract

BACKGROUND: Ischemic heart disease is a leading cause of death globally with a disproportionate burden in low- and middle-income countries (LMICs). Natural language processing (NLP) allows for data enrichment in large datasets to facilitate key clinical research. We used NLP to assess gender differences in symptoms and management of patients hospitalized with acute myocardial infarction (AMI) at Aga Khan University Hospital-Pakistan.

Authors

  • Christine Ngaruiya
    Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, United States.
  • Zainab Samad
    Department of Medicine, Aga Khan University, Karachi, Pakistan.
  • Salma Tajuddin
    Department of Medicine, Aga Khan University, Karachi, Pakistan.
  • Zarmeen Nasim
    CITRIC Health Data Science Center, Aga Khan University, Karachi, Pakistan.
  • Rebecca Leff
    Department of Emergency Medicine, Mayo Clinic School of Graduate Medical Education, Rochester, MN, United States.
  • Awais Farhad
    Department of Medicine, Aga Khan University, Karachi, Pakistan.
  • Kyle Pires
    Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, United States.
  • Muhammad Alamgir Khan
    School of Medicine, Aga Khan University, Karachi, Pakistan.
  • Lauren Hartz
    Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, United States.
  • Basmah Safdar

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