AZDrugMiner: An information extraction system for mining patient-reported adverse drug events in online patient forums

Xiao Liu, Hsinchun Chen

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

71 Scopus citations

Abstract

Post-marketing drug surveillance is a critical component of drug safety. Drug regulatory agencies such as the U.S. Food and Drug Administration (FDA) rely on voluntary reports from health professionals and consumers contributed to its FDA Adverse Event Reporting System (FAERS) to identify adverse drug events (ADEs). However, it is widely known that FAERS underestimates the prevalence of certain adverse events. Popular patient social media sites such as DailyStrength and PatientsLikeMe provide new information sources from which patient-reported ADEs may be extracted. In this study, we propose an analytical framework for extracting patient-reported adverse drug events from online patient forums. We develop a novel approach - the AZDrugMiner system - based on statistical learning to extract ad-verse drug events in patient discussions and identify reports from patient experiences. We evaluate our system using a set of manually annotated forum posts which show promising performance. We also examine correlations and differences between patient ADE reports extracted by our system and reports from FAERS. We conclude that patient social media ADE reports can be extracted effectively using our proposed framework. Those patient reports can reflect unique perspectives in treatment and be used to improve patient care and drug safety.

Original languageEnglish (US)
Title of host publicationSmart Health - International Conference, ICSH 2013, Proceedings
Pages134-150
Number of pages17
DOIs
StatePublished - Aug 12 2013
Event2013 International Conference for Smart Health, ICSH 2013 - Beijing, China
Duration: Aug 3 2013Aug 4 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8040 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other2013 International Conference for Smart Health, ICSH 2013
CountryChina
CityBeijing
Period8/3/138/4/13

Keywords

  • Adverse Drug Events
  • Health Big Data
  • Health Social Media Analytics
  • Information Extraction
  • Patient Forums

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Liu, X., & Chen, H. (2013). AZDrugMiner: An information extraction system for mining patient-reported adverse drug events in online patient forums. In Smart Health - International Conference, ICSH 2013, Proceedings (pp. 134-150). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8040 LNCS). https://doi.org/10.1007/978-3-642-39844-5_16