Lookup NU author(s): Dr YiChuan Wang
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© 2018 The aim of this study is to propose an automatic and real-time social media analytics framework with interactive data visualizations to support effective exploration of knowledge about adverse drug reaction (ADR) surveillance. This proposed framework has been prototypically implemented on the basis of social media data. A longitudinal diabetes patient online community data (AskaPatient.com) as well as FDA Adverse Event Reporting Systems (FAERS) data as a benchmark were used to evaluate our proposed approach's performance. Based on the results, our approach significantly increases the precision and accuracy for ADR extraction. The number of ADR cases, the time when the ADRs occurred, and the rating of Glucophage have been visualized that resulted by mining a collection of 870 ADRs posted in Askapatents.com over a certain time period (from 2001 to 2015). The results have important implications for pharmaceutical companies and hospitals wishing to monitor ADRs of medicines.
Author(s): Li S, Yu C-H, Wang Y, Babu Y
Publication type: Article
Publication status: Published
Journal: International Journal of Information Management
Pages: Epub ahead of print
Online publication date: 23/01/2019
Acceptance date: 18/12/2018
ISSN (print): 0268-4012
ISSN (electronic): 1873-4707
Publisher: Pergamon Press
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