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

Adverse Drug Events Detection, Pharmacovigilance and Surveillance (143) Natural Language Processing (636) Pharmacovigilance (125) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1552) Infodemiology and Infoveillance (1269) Machine Learning (1370) Substance Abuse (379)

Published on in Vol 10, No 9 (2022): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/38140, first published .
An Assessment of Mentions of Adverse Drug Events on Social Media With Natural Language Processing: Model Development and Analysis

An Assessment of Mentions of Adverse Drug Events on Social Media With Natural Language Processing: Model Development and Analysis

An Assessment of Mentions of Adverse Drug Events on Social Media With Natural Language Processing: Model Development and Analysis

Authors of this article:

Deahan Yu1 Author Orcid Image ;   V G Vinod Vydiswaran1, 2 Author Orcid Image
Article Authors Cited by (10) Tweetations (6) Metrics

Journals

  1. Lin S, Cheng X, Zhang J, Yannam J, Barnes A, Koch J, Hayes R, Gimm G, Zhao X, Purohit H, Xue H. Social Media Data Mining of Antitobacco Campaign Messages: Machine Learning Analysis of Facebook Posts. Journal of Medical Internet Research 2023;25:e42863 View
  2. Raza S, Schwartz B, Lakamana S, Ge Y, Sarker A. A framework for multi-faceted content analysis of social media chatter regarding non-medical use of prescription medications. BMC Digital Health 2023;1(1) View
  3. Boonstra M, Weissenbacher D, Moore J, Gonzalez-Hernandez G, Asselbergs F. Artificial intelligence: revolutionizing cardiology with large language models. European Heart Journal 2024;45(5):332 View
  4. Ngamwal S, Yogita , Pal V. Sequence Labelling with 2 Level Segregation (SL2LS): A framework to extract COVID-19 vaccine adverse drug reactions from Twitter data. Expert Systems with Applications 2024;249:123572 View
  5. Allan S, Ward T, Eisner E, Bell I, Cella M, Chaudhry I, Torous J, Kiran T, Kabir T, Priyam A, Richardson C, Reininghaus U, Schick A, Schwannauer M, Syrett S, Zhang X, Bucci S. Adverse Events Reporting in Digital Interventions Evaluations for Psychosis: A Systematic Literature Search and Individual Level Content Analysis of Adverse Event Reports. Schizophrenia Bulletin 2024 View
  6. Nishioka S, Watabe S, Yanagisawa Y, Sayama K, Kizaki H, Imai S, Someya M, Taniguchi R, Yada S, Aramaki E, Hori S. Adverse Event Signal Detection Using Patients’ Concerns in Pharmaceutical Care Records: Evaluation of Deep Learning Models. Journal of Medical Internet Research 2024;26:e55794 View
  7. Dong F, Guo W, Liu J, Patterson T, Hong H. BERT-based language model for accurate drug adverse event extraction from social media: implementation, evaluation, and contributions to pharmacovigilance practices. Frontiers in Public Health 2024;12 View
  8. Golder S, O'Connor K, Wang Y, Klein A, Gonzalez Hernandez G. The value of social media analysis for adverse events detection and pharmacovigilance: a Scoping Review (Preprint). JMIR Public Health and Surveillance 2024 View
  9. Sinruwng N, Yogita , Pal V, Verma A. DARCVAA: A Deep Neural Networks Based Framework for Detecting Adverse Reactions of COVID-19 Vaccines and Association Analysis. SN Computer Science 2024;5(5) View
  10. Karapetiantz P, Audeh B, Redjdal A, Tiffet T, Bousquet C, Jaulent M. Monitoring Adverse Drug Events in Web Forums: Evaluation of a Pipeline and Use Case Study. Journal of Medical Internet Research 2024;26:e46176 View