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

Infodemiology and Infoveillance (1053) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (869) Adverse Drug Events Detection, Pharmacovigilance and Surveillance (93) Pharmacovigilance (83) Natural Language Processing (509) New Methods (701) Machine Learning (1059) Peer-to-Peer Support and Online Communities (485) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1327)

Published on in Vol 23 , No 10 (2021) :October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/27714, first published .
Quantifying the Severity of Adverse Drug Reactions Using Social Media: Network Analysis

Quantifying the Severity of Adverse Drug Reactions Using Social Media: Network Analysis

Quantifying the Severity of Adverse Drug Reactions Using Social Media: Network Analysis

Authors of this article:

Adam Lavertu 1, 2 Author Orcid Image ;   Tymor Hamamsy 3 Author Orcid Image ;   Russ B Altman 2, 4, 5 Author Orcid Image
Article Authors Cited by (3) Tweetations (7) Metrics

Journals

  1. Carpenter K, Altman R. Using GPT-3 to Build a Lexicon of Drugs of Abuse Synonyms for Social Media Pharmacovigilance. Biomolecules 2023;13(2):387 View
  2. Tan H, Teo C, Ang P, Loke W, Tham M, Tan S, Soh B, Foo P, Ling Z, Yip W, Tang Y, Yang J, Tung K, Dorajoo S. Combining Machine Learning with a Rule-Based Algorithm to Detect and Identify Related Entities of Documented Adverse Drug Reactions on Hospital Discharge Summaries. Drug Safety 2022;45(8):853 View
  3. Pétervári M, Benczik B, Balogh O, Petrovich B, Ágg B, Ferdinandy P. Network Analysis for Signal Detection in Spontaneous Adverse Event Reporting Database: Application of Network Weighting Normalization to Characterize Cardiovascular Drug Safety. Drug Safety 2022;45(11):1423 View