Identifying adverse drug events from health social media: A case study on heart disease discussion forums

Xiao Liu, Jing Liu, Hsinchun Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

24 Scopus citations


Health social media sites have emerged as major platforms for discussions of treatments and drug side effects, making them a promising source for listening to patients' voices in adverse drug event reporting. However, extracting patient adverse drug event reports from social media continues to be a challenge in health informatics research. To utilize the fertile health social media data for drug safety research, we develop advanced information extraction techniques for identifying adverse drug events in health social media. A case study is conducted on a heart disease discussion forum to evaluate the performance. Our approach achieves an f-measure of 82% in the recognition of medical events and treatments, an f-measure of 69% for identifying adverse drug events and an f-measure of 90% in patient report extraction. Analysis on the extracted adverse drug events suggests that health social media can provide supplemental information for adverse drug events and drug interactions. It provides a less biased insight into the distribution of adverse events among heart disease population compared to data from a drug regulatory agency.

Original languageEnglish (US)
Title of host publicationSmart Health - International Conference, ICSH 2014, Proceedings
Number of pages12
ISBN (Print)9783319084152
StatePublished - 2014
Event2nd International Conference for Smart Health, CSH 2014 - Beijing, China
Duration: Jul 10 2014Jul 11 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8549 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other2nd International Conference for Smart Health, CSH 2014


  • Adverse drug event extraction
  • Health social media analytics
  • Heart disease
  • Medical entity extraction
  • Statistical learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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