Estimation of the prevalence of adverse drug reactions from social media

International Journal of Medical Informatics
Thin NguyenHelen Christensen

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.

Citations

Oct 5, 2018·Expert Opinion on Drug Safety·Irma ConvertinoMarco Tuccori
Jan 3, 2020·Neuropsychopharmacology : Official Publication of the American College of Neuropsychopharmacology·Aaron M Bornstein, Hanna Pickard
Jun 19, 2020·Annual Review of Biomedical Data Science·Rion Brattig CorreiaLuis M Rocha
Jan 9, 2020·Cancer Control : Journal of the Moffitt Cancer Center·Muhammad AmithCui Tao
Jun 4, 2019·Frontiers in Pharmacology·Pantelis NatsiavasVassilis Koutkias

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