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Exploring adverse drug reactions of diabetes medicine using social media analytics and interactive visualizations
Institution:1. Xinhua College of Sun Yat-Sen University, China;2. Graduate Institute of Technology, Innovation & Intellectual Property Management, National Chengchi University (NCCU), Taiwan, ROC;3. Newcastle University Business School, Newcastle University, United Kingdom;4. The Home Depot, USA;1. Department of Information Systems and Business Analytics, Hofstra University, 11501, USA;2. H. John Heinz III College of Information Systems and Public Policy, Carnegie Mellon University, 15213, USA;1. University of Rome Tor Vergata, Department of Enterprise Engineering, Via del Politecnico 1, 00133 Rome, Italy;2. MIT Center for Collective Intelligence, 245 First Street, 02142 Cambridge, MA, USA;1. Petróleo Brasileiro S.A.- Petrobras University, Rua Ulysses Guimarães, 565 – Cidade Nova, Rio de Janeiro, RJ, 20.211-225, Brazil;2. Fluminense Federal University, Faculty of Engineering, Department of Industrial Engineering, Industrial Engineering Graduate Program, Rua Passo da Pátria, 156, bloco D, Sala 306, São Domingos, Niterói, RJ, 24.210-240, Brazil;1. IMT Atlantique, LEMNA, 4 rue Alfred Kastler, BP 20722, 44307 Nantes Cedex 3, France;2. University of Nantes, LEMNA, SKEMA Business School, Chemin de la Censive du Tertre, BP 52231, 44322 Nantes Cedex 3, France;3. University of South Florida, Muma College of Business, 4202 E. Fowler Avenue, BSN 3403, Tampa, FL 33620, USA
Abstract: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.
Keywords:Social media analytics  Adverse drug reactions (ADRs)  Data visualization  Big data analytics  Online health community
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