首页|Filtering big data from social media - Building an early warning system for adverse drug reactions
Filtering big data from social media - Building an early warning system for adverse drug reactions
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NSTL
Elsevier
Objectives: Adverse drug reactions (ADRs) are believed to be a leading cause of death in the world. Pharmacovigilance systems are aimed at early detection of ADRs. With the popularity of social media, Web forums and discussion boards become important sources of data for consumers to shake their drug use experience, as a result may provide useful information on drugs and their adverse reactions. In this study, we propose an automated ADR related posts filtering mechanism using text classification methods. In real-life settings, ADR related messages are highly distributed in social media, while non-ADR related messages are unspecific and topically diverse. It is expensive to manually label a large amount of ADR related messages (positive examples) and non-ADR related messages (negative examples) to train classification systems. To mitigate this challenge, we examine the use of a partially supervised learning classification method to automate the process.
Partially supervised classificationLatent Dirichlet Allocation (LDA)Adverse drug reactionsSocial media filteringSocial media mining
Yang, Ming、Kiang, Melody、Shang, Wei
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Cent Univ Finance & Econ, Sch Informat, Dept Informat Management, Beijing 100081, Peoples R China
Calif State Univ Long Beach, Dept Informat Syst, Long Beach, CA 90840 USA
Acad Math & Syst Sci, Beijing 100190, Peoples R China
NSTL
Elsevier
