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

AI Language Models in Health Care (159) Electronic Health Records (1259) Generative Language Models Including ChatGPT (618) Clinical Information and Decision Making (1790) Artificial Intelligence (1969) Vulnerable Populations in Health Research (183) Language Processing, Classifiers, and Syndrome Definitions (46) Ontologies, Classifications, and Coding (305)

Published on in Vol 13 (2025)

This is a member publication of University of Toronto

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/68955, first published .
Efficient Detection of Stigmatizing Language in Electronic Health Records via In-Context Learning: Comparative Analysis and Validation Study

Efficient Detection of Stigmatizing Language in Electronic Health Records via In-Context Learning: Comparative Analysis and Validation Study

Efficient Detection of Stigmatizing Language in Electronic Health Records via In-Context Learning: Comparative Analysis and Validation Study

Authors of this article:

Hongbo Chen1 Author Orcid Image ;   Myrtede Alfred1 Author Orcid Image ;   Eldan Cohen1 Author Orcid Image
Article Authors Cited by Tweetations Metrics

Hongbo Chen   1 , MSc ;   Myrtede Alfred   1 , PhD ;   Eldan Cohen   1 , PhD

1 Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada

Corresponding Author:

  • Eldan Cohen, PhD
  • Department of Mechanical and Industrial Engineering
  • University of Toronto
  • 27 King's College Circle
  • Toronto, ON M5S 1A1
  • Canada
  • Phone: 1 416 978 4184
  • Email: ecohen@mie.utoronto.ca