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

Public (e)Health, Digital Epidemiology and Public Health Informatics (641) Public Health Informatics (79) Infodemiology and Infoveillance (1274) Pharmacovigilance (128) Machine Learning (1385) Adverse Drug Events Detection, Pharmacovigilance and Surveillance (146) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1570)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/43630, first published .
Automating Detection of Drug-Related Harms on Social Media: Machine Learning Framework

Automating Detection of Drug-Related Harms on Social Media: Machine Learning Framework

Automating Detection of Drug-Related Harms on Social Media: Machine Learning Framework

Authors of this article:

Andrew Fisher1 Author Orcid Image ;   Matthew Maclaren Young2, 3, 4 Author Orcid Image ;   Doris Payer2 Author Orcid Image ;   Karen Pacheco5 Author Orcid Image ;   Chad Dubeau2 Author Orcid Image ;   Vijay Mago6 Author Orcid Image
Article Authors Cited by (1) Tweetations (6) Metrics

Journals

  1. Castillo-Toledo C, Fraile-Martínez O, Donat-Vargas C, Lara-Abelenda F, Ortega M, Garcia-Montero C, Mora F, Alvarez-Mon M, Quintero J, Alvarez-Mon M. Insights from the Twittersphere: a cross-sectional study of public perceptions, usage patterns, and geographical differences of tweets discussing cocaine. Frontiers in Psychiatry 2024;15 View