Towards Large-scale Twitter Mining for Drug-related Adverse Events

Drug-related adverse events pose substantial risks to patients who consume post-market or Drug-related adverse events pose substantial risks to patients who consume post-market or investigational drugs. Early detection of adverse events benefits not only the drug regulators, but also the manufacture...

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Published in:SHB'12 : proceedings of the 2012 ACM International Workshop on Smart Health and Wellbeing : October 29, 2012, Maui, Hawaii, USA. International Workshop on Smart Health and Wellbeing (2012 : Maui, Hawaii) Vol. 2012; p. 25
Main Authors: Bian, Jiang, Topaloglu, Umit, Yu, Fan
Format: Journal Article
Language:English
Published: United States 29.10.2012
Subjects:
Public Health
Algorithms
High Performance Computing
Theory
Twitter mining
Natural Language Processing
Big-data Analytic
Experimentation
Drug-related Adverse Events
MapReduce
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Summary:Drug-related adverse events pose substantial risks to patients who consume post-market or Drug-related adverse events pose substantial risks to patients who consume post-market or investigational drugs. Early detection of adverse events benefits not only the drug regulators, but also the manufacturers for pharmacovigilance. Existing methods rely on patients' "spontaneous" self-reports that attest problems. The increasing popularity of social media platforms like the Twitter presents us a new information source for finding potential adverse events. Given the high frequency of user updates, mining Twitter messages can lead us to real-time pharmacovigilance. In this paper, we describe an approach to find drug users and potential adverse events by analyzing the content of twitter messages utilizing Natural Language Processing (NLP) and to build Support Vector Machine (SVM) classifiers. Due to the size nature of the dataset (i.e., 2 billion Tweets), the experiments were conducted on a High Performance Computing (HPC) platform using MapReduce, which exhibits the trend of big data analytics. The results suggest that daily-life social networking data could help early detection of important patient safety issues.
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DOI:10.1145/2389707.2389713