Novel Algorithms for Improved Pattern Recognition Using the US FDA Adverse Event Network Analyzer

Taxiarchis Botsis, John Scott, Ravi Goud, Pamela Toman, Andrea Sutherland, Robert Ball

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

5 Scopus citations


The medical review of adverse event reports for medical products requires the processing of 'big data' stored in spontaneous reporting systems, such as the US Vaccine Adverse Event Reporting System (VAERS). VAERS data are not well suited to traditional statistical analyses so we developed the FDA Adverse Event Network Analyzer (AENA) and three novel network analysis approaches to extract information from these data. Our new approaches include a weighting scheme based on co-occurring triplets in reports, a visualization layout inspired by the islands algorithm, and a network growth methodology for the detection of outliers. We explored and verified these approaches by analysing the historical signal of Intussusception (IS) after the administration of RotaShield vaccine (RV) in 1999. We believe that our study supports the use of AENA for pattern recognition in medical product safety and other clinical data.

Original languageEnglish (US)
Title of host publicatione-Health - For Continuity of Care - Proceedings of MIE 2014
EditorsLouise Pape-Haugaard, Brigitte Seroussi Brigitte, Osman Saka, Christian Lovis, Arie Hasman, Stig Kjaer Andersen
PublisherIOS Press
Number of pages5
ISBN (Electronic)9781614994312
StatePublished - 2014
Event25th European Medical Informatics Conference, MIE 2014 - Istanbul, Turkey
Duration: Aug 31 2014Sep 3 2014

Publication series

NameStudies in Health Technology and Informatics
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365


Other25th European Medical Informatics Conference, MIE 2014


  • Big Data
  • Network Analysis
  • Pattern Recognition
  • Safety Surveillance

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management


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