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

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

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