This paper is in the following e-collection/theme issue:

Formative Evaluation of Digital Health Interventions (2491) Artificial Intelligence (1421) Clinical Information and Decision Making (1434) mHealth in the Developing World/LMICs, Underserved Communities, and for Global Health (578) Myocardial Infarction (Heart Attack) (45) Natural Language Processing (706)

Published on in Vol 8 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/42774, first published .
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

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

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

Authors of this article:

Christine Ngaruiya1, 2 Author Orcid Image ;   Zainab Samad3 Author Orcid Image ;   Salma Tajuddin3, 4 Author Orcid Image ;   Zarmeen Nasim4 Author Orcid Image ;   Rebecca Leff5 Author Orcid Image ;   Awais Farhad3 Author Orcid Image ;   Kyle Pires1 Author Orcid Image ;   Muhammad Alamgir Khan6 Author Orcid Image ;   Lauren Hartz1 Author Orcid Image ;   Basmah Safdar1 Author Orcid Image
Article Authors Cited by Tweetations Metrics

Christine Ngaruiya   1, 2 * , MSc, DTM&H, MD ;   Zainab Samad   3 , MHS, MBBS ;   Salma Tajuddin   3, 4 , MCS ;   Zarmeen Nasim   4 , PhD ;   Rebecca Leff   5 , MD ;   Awais Farhad   3 , MBBS ;   Kyle Pires   1 , MD ;   Muhammad Alamgir Khan   6 , MBBS ;   Lauren Hartz   1 * ;   Basmah Safdar   1 , MSc, MD

1 Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, United States

2 Department of Emergency Medicine, Stanford School of Medicine, Palo Alto, CA, United States

3 Department of Medicine, Aga Khan University, Karachi, Pakistan

4 CITRIC Health Data Science Center, Aga Khan University, Karachi, Pakistan

5 Department of Emergency Medicine, Mayo Clinic School of Graduate Medical Education, Rochester, MN, United States

6 School of Medicine, Aga Khan University, Karachi, Pakistan

*these authors contributed equally

Corresponding Author:

  • Christine Ngaruiya, MSc, DTM&H, MD
  • Department of Emergency Medicine
  • Yale School of Medicine
  • 464 Congress Avenue, Suite #260
  • New Haven, CT, 06519
  • United States
  • Phone: 1 2037852353
  • Email: christine.ngaruiya@yale.edu