Identifying adverse drug events from patient social media: A case study for diabetes

Xiao Liu, Hsinchun Chen

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

Abstract

Patient social media sites have emerged as major platforms for discussion of treatments and drug side effects, making them a promising source for listening to patients' voices in adverse drug event reporting. However, extracting patient reports from social media continues to be a challenge in health informatics research. In light of the need for more robust extraction methods, the authors developed a novel information extraction framework for identifying adverse drug events from patient social media. They also conducted a case study on a major diabetes patient social media platform to evaluate their framework's performance. Their approach achieves an f-measure of 86 percent in recognizing discussion of medical events and treatments, an f-measure of 69 percent for identifying adverse drug events, and an f-measure of 84 percent in patient report extraction. Their proposed methods significantly outperformed prior work in extracting patient reports of adverse drug events in health social media.

Original languageEnglish (US)
Pages (from-to)44-51
Number of pages8
JournalIEEE Intelligent Systems
Volume30
Issue number3
DOIs
StatePublished - May 1 2015

Keywords

  • ADE
  • adverse drug effects
  • clinical trials
  • diabetes
  • health
  • intelligent systems
  • predictive analytics
  • social media

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Artificial Intelligence

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