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

Infodemiology and Infoveillance (1558) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1139) Adverse Drug Events Detection, Pharmacovigilance and Surveillance (215) Pharmacovigilance (171) Natural Language Processing (917) New Methods (1134) Machine Learning (2076) Peer-to-Peer Support and Online Communities (797) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1911)

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 Lavertu1, 2 Author Orcid Image ;   Tymor Hamamsy3 Author Orcid Image ;   Russ B Altman2, 4, 5 Author Orcid Image
Article Authors Cited by (13) 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
  4. Shieh C, Nasongkhla J. Effects of motivation to use social networking sites on students’ media literacy and critical thinking. Online Journal of Communication and Media Technologies 2024;14(1):e202404 View
  5. Yue Q, Ding R, Chen W, Wu L, Liu K, Ji Z. Mining Real-World Big Data to Characterize Adverse Drug Reaction Quantitatively: Mixed Methods Study. Journal of Medical Internet Research 2024;26:e48572 View
  6. van den Bemt P, van Puijenbroek E, van Hunsel F. How could employing the patient perspective transform pharmacovigilance?. Expert Opinion on Drug Safety 2024;23(7):793 View
  7. Shen X, Zhang Y, Li J, Zhou Y, Butensky S, Zhang Y, Cai Z, DeWan A, Khan S, Yan H, Johnson C, Zhu F. OncoSexome: the landscape of sex-based differences in oncologic diseases. Nucleic Acids Research 2025;53(D1):D1443 View
  8. Wang N, Taylor C. DrIVeNN: Drug Interaction Vectors Neural Network. Journal of Computational Biology 2025;32(7):696 View
  9. Dong F, Guo W, Liu J, Patterson T, Hong H. Pharmacovigilance in the digital age: gaining insight from social media data. Experimental Biology and Medicine 2025;250 View
  10. Mukherjee S, Roy S, Era N. Patient Voices in Pharmacovigilance: Understanding Adverse Drug Reactions Through Consumer Narratives. Journal of Evaluation in Clinical Practice 2025;31(4) View
  11. Bravo F, Chen Y, Silberholz J. Enhancing Safety Signaling: Integrating Clinical Trials and Post-Marketing Adverse Event Reports. SSRN Electronic Journal 2025 View

Books/Policy Documents

  1. Montero-Colio M, Salas-Zárate M, Paredes-Valverde M. Technologies and Innovation. View
  2. Chakraborty A, Venkatraman J. The Quintessence of Basic and Clinical Research and Scientific Publishing. View