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

Artificial Intelligence (1630) Natural Language Processing (770) Pharmacovigilance (153) mHealth for Data Collection and Research (1048) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1756) Machine Learning (1654) Medication Reconciliation (16) Generative Language Models Including ChatGPT (442) Infodemiology and Infoveillance (1433)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/54601, first published .
Using Large Language Models to Detect and Understand Drug Discontinuation Events in Web-Based Forums: Development and Validation Study

Using Large Language Models to Detect and Understand Drug Discontinuation Events in Web-Based Forums: Development and Validation Study

Using Large Language Models to Detect and Understand Drug Discontinuation Events in Web-Based Forums: Development and Validation Study

Authors of this article:

William Trevena1 Author Orcid Image ;   Xiang Zhong1 Author Orcid Image ;   Michelle Alvarado1 Author Orcid Image ;   Alexander Semenov1 Author Orcid Image ;   Alp Oktay2 Author Orcid Image ;   Devin Devlin3 Author Orcid Image ;   Aarya Yogesh Gohil1 Author Orcid Image ;   Sai Harsha Chittimouju1 Author Orcid Image
Article Authors Cited by Tweetations Metrics

William Trevena   1 , PhD ;   Xiang Zhong   1 , PhD ;   Michelle Alvarado   1 , PhD ;   Alexander Semenov   1 , PhD ;   Alp Oktay   2 , BSc ;   Devin Devlin   3 , BSc ;   Aarya Yogesh Gohil   1 , MSc ;   Sai Harsha Chittimouju   1 , MSc

1 Department of Industrial and Systems Engineering, The University of Florida, GAINESVILLE, FL, United States

2 Department of Industrial and Systems Engineering, The University of San Diego, San Diego, CA, United States

3 Microsoft, Seattle, WA, United States

Corresponding Author:

  • Xiang Zhong, PhD
  • Department of Industrial and Systems Engineering
  • The University of Florida
  • PO BOX 115002
  • GAINESVILLE, FL, 32611-5002
  • United States
  • Phone: 1 3523922477
  • Email: xiang.zhong@ise.ufl.edu