Postmarketing Drug Safety Surveillance Using Publicly Available Health-Consumer-Contributed Content in Social Media

Abstract

Postmarketing drug safety surveillance is important because many potential adverse drug reactions cannot be identified in the premarketing review process. It is reported that about 5% of hospital admissions are attributed to adverse drug reactions and many deaths are eventually caused, which is a serious concern in public health. Currently, drug safety detection relies heavily on voluntarily reporting system, electronic health records, or relevant databases. There is often a time delay before the reports are filed and only a small portion of adverse drug reactions experienced by health consumers are reported. Given the popularity of social media, many health social media sites are now available for health consumers to discuss any health-related issues, including adverse drug reactions they encounter. There is a large volume of health-consumer-contributed content available, but little effort has been made to harness this information for postmarketing drug safety surveillance to supplement the traditional approach. In this work, we propose the association rule mining approach to identify the association between a drug and an adverse drug reaction. We use the alerts posted by Food and Drug Administration as the gold standard to evaluate the effectiveness of our approach. The result shows that the performance of harnessing health-related social media content to detect adverse drug reaction is good and promising.

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Index Terms

• Applied computing

  • Document management and text processing

    • Document preparation

      • Multi / mixed media creation

• Computing methodologies

  • Artificial intelligence

    • Natural language processing

• Human-centered computing

  • Human computer interaction (HCI)

    • Interaction paradigms

      • Hypertext / hypermedia

• Information systems

  • Information retrieval

    • Document representation

      • Content analysis and feature selection

  • Information systems applications

    • Data mining