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Estimation of the prevalence of adverse drug reactions from social media
journal contribution
posted on 2017-06-01, 00:00 authored by Thin NguyenThin Nguyen, M E Larsen, B O'Dea, Quoc-Dinh Phung, Svetha VenkateshSvetha Venkatesh, H ChristensenThis 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.
History
Journal
International journal of medical informaticsVolume
102Pagination
130 - 137Publisher
ElsevierLocation
Amsterdam, The NetherlandsPublisher DOI
eISSN
1872-8243Language
engPublication classification
C Journal article; C1 Refereed article in a scholarly journalCopyright notice
2017, ElsevierUsage metrics
Keywords
Adverse drug reactionsConsumer health informaticsDrug informaticsSocial mediaWord embeddingWord representationScience & TechnologyTechnologyLife Sciences & BiomedicineComputer Science, Information SystemsHealth Care Sciences & ServicesMedical InformaticsComputer ScienceHEALTH-PROMOTIONFACEBOOKDistributed Computing
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