Identifying Adverse Drug Events in Twitter Data Using Semi-Supervised Bootstrapped Lexicons

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dc.contributor.advisor Xue, Nianwen
dc.contributor.author Benzschawel, Eric
dc.date.accessioned 2016-05-07T00:20:49Z
dc.date.available 2016-05-07T00:20:49Z
dc.date.issued 2016
dc.identifier.uri http://hdl.handle.net/10192/32253
dc.description.abstract Adverse drug event (ADE) detection serves as a primary quality of care benchmark in healthcare and plays a major role in pharmacovigilance. Social media data represents a largely untapped source of public clinical narratives which can be used to expand existing ADE tracking systems. Existing studies focus on annotation of small amounts of data to handle non-standard language usage. This study presents a new application of semi-supervised lexicon bootstrapping to flag Twitter data for potential ADEs. To do this, a new corpus sixteen times larger than the current largest, publicly available dataset was constructed and used to generate robust, bootstrapped drug and medical event lexicons. These lexicons were applied to held-out data to flag tweets containing potential ADEs. Compared to recent studies of lexicon-based ADE detection in Twitter, this method achieved competitive F1 scores and offers a robust evaluation capable of identifying severe ADEs in the social media sphere, representing important new data points relevant to existing pharmacovigilance systems.
dc.description.sponsorship Brandeis University, Graduate School of Arts and Sciences
dc.format.mimetype application/pdf
dc.language English
dc.language.iso eng
dc.publisher Brandeis University
dc.relation.ispartofseries Brandeis University Theses and Dissertations
dc.rights Copyright by Eric Benzschawel 2016
dc.subject natural language processing
dc.subject social media
dc.subject twitter
dc.subject clinical NLP
dc.subject pharmacovigilance
dc.subject bootstrapping
dc.subject lexicon-based techniques
dc.subject ADE
dc.subject ADR
dc.subject adverse effect
dc.title Identifying Adverse Drug Events in Twitter Data Using Semi-Supervised Bootstrapped Lexicons
dc.type Thesis
dc.contributor.department Graduate Program in Computational Linguistics
dc.degree.name MA
dc.degree.level Masters
dc.degree.discipline Computational Linguistics
dc.degree.grantor Brandeis University, Graduate School of Arts and Sciences


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