Estimation of the prevalence of adverse drug reactions from social media. (June 2017)

• Record Type: Journal Article
• Authors: Nguyen, Thin; Larsen, Mark E.; O'Dea, Bridianne; Phung, Dinh; Venkatesh, Svetha; Christensen, Helen
• Is Part Of: International journal of medical informatics. Volume 102(2017)
• Page Start: 130
• Page End: 137
• Publication Date: 2017-06
• Subjects: Consumer health informatics -- Drug informatics -- Adverse drug reactions -- Social media -- Word representation -- Word embedding; Medical informatics -- Periodicals; Information science -- Periodicals; Computers -- Periodicals; Medical technology -- Periodicals; Medical Informatics -- Periodicals; Technology, Medical -- Periodicals; Computers; Information science; Medical informatics; Medical technology; Electronic journals; Periodicals; Electronic journals; 610.285
• DOI: 10.1016/j.ijmedinf.2017.03.013
• Languages: English
• ISSNs: 1386-5056

Abstract

Abstract : Highlights: Word2vec was utilized to discover variants of adverse drug reaction (ADR) terms in social media data. The prevalence of ADR derived from social media using original lexicon has a decent correlation with that from official sources. The lexicon derived by word2vec improved the correlation. Advanced cluster computing was employed to process 6.4 terabytes of data containing 3.8 billion records. Abstract: This work aims to estimate the degree of adverse drug reactions (ADR) for psychiatric medications from social media, including Twitter, Reddit, and LiveJournal. Advances in lightning-fast cluster computing was employed to process large scale data, consisting of 6.4 terabytes of data containing 3.8 billion records from all the media. Rates of ADR were quantified using the SIDER database of drugs and side-effects, and an estimated ADR rate was based on the prevalence of discussion in the social media corpora. Agreement between these measures for a sample of ten popular psychiatric drugs was evaluated using the Pearson correlation coefficient, r, with values between 0.08 and 0.50. Word2vec, a novel neural learning framework, was utilized to improve the coverage of variants of ADR terms in the unstructured text by identifying syntactically or semantically similar terms. Improved correlation coefficients, between 0.29 and 0.59, demonstrates the capability of advanced techniques in machine learning to aid in the discovery of meaningful patterns from medical data, and social media data, at scale.