Literature DB >> 25991101

Exploring brand-name drug mentions on Twitter for pharmacovigilance.

Pablo Carbonell1, Miguel A Mayer1, Àlex Bravo1.   

Abstract

Twitter has been proposed by several studies as a means to track public health trends such as influenza and Ebola outbreaks by analyzing user messages in order to measure different population features and interests. In this work we analyze the number and features of mentions on Twitter of drug brand names in order to explore the potential usefulness of the automated detection of drug side effects and drug-drug interactions on social media platforms such as Twitter. This information can be used for the development of predictive models for drug toxicity, drug-drug interactions or drug resistance. Taking into account the large number of drug brand mentions that we found on Twitter, it is promising as a tool for the detection, understanding and monitoring the way people manage prescribed drugs.

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Year:  2015        PMID: 25991101

Source DB:  PubMed          Journal:  Stud Health Technol Inform        ISSN: 0926-9630


  9 in total

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Journal:  J Biomed Inform       Date:  2019-10-15       Impact factor: 6.317

3.  Content analysis of Twitter in relation to biological treatments for chronic inflammatory arthropathies: an exploratory study.

Authors:  Noemí Martínez-López De Castro; Marisol Samartín-Ucha; Alicia Martín-Vila; Miriam Álvarez-Payero; Guadalupe Piñeiro-Corrales; José M Pego-Reigosa
Journal:  Eur J Hosp Pharm       Date:  2018-01-24

4.  Analysis of Patient Narratives in Disease Blogs on the Internet: An Exploratory Study of Social Pharmacovigilance.

Authors:  Shinichi Matsuda; Kotonari Aoki; Shiho Tomizawa; Masayoshi Sone; Riwa Tanaka; Hiroshi Kuriki; Yoichiro Takahashi
Journal:  JMIR Public Health Surveill       Date:  2017-02-24

5.  Deep neural networks ensemble for detecting medication mentions in tweets.

Authors:  Davy Weissenbacher; Abeed Sarker; Ari Klein; Karen O'Connor; Arjun Magge; Graciela Gonzalez-Hernandez
Journal:  J Am Med Inform Assoc       Date:  2019-12-01       Impact factor: 4.497

6.  Language-agnostic pharmacovigilant text mining to elicit side effects from clinical notes and hospital medication records.

Authors:  Benjamin Skov Kaas-Hansen; Davide Placido; Cristina Leal Rodríguez; Hans-Christian Thorsen-Meyer; Simona Gentile; Anna Pors Nielsen; Søren Brunak; Gesche Jürgens; Stig Ejdrup Andersen
Journal:  Basic Clin Pharmacol Toxicol       Date:  2022-07-26       Impact factor: 3.688

7.  Establishing a Link Between Prescription Drug Abuse and Illicit Online Pharmacies: Analysis of Twitter Data.

Authors:  Takeo Katsuki; Tim Ken Mackey; Raphael Cuomo
Journal:  J Med Internet Res       Date:  2015-12-16       Impact factor: 5.428

Review 8.  Translational Biomedical Informatics and Pharmacometrics Approaches in the Drug Interactions Research.

Authors:  Pengyue Zhang; Heng-Yi Wu; Chien-Wei Chiang; Lei Wang; Samar Binkheder; Xueying Wang; Donglin Zeng; Sara K Quinney; Lang Li
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2018-01-09

Review 9.  Utility of social media and crowd-intelligence data for pharmacovigilance: a scoping review.

Authors:  Andrea C Tricco; Wasifa Zarin; Erin Lillie; Serena Jeblee; Rachel Warren; Paul A Khan; Reid Robson; Ba' Pham; Graeme Hirst; Sharon E Straus
Journal:  BMC Med Inform Decis Mak       Date:  2018-06-14       Impact factor: 2.796

  9 in total

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