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

Abstract

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 publicationStudies in Health Technology and Informatics
PublisherIOS Press
Pages1178-1182
Number of pages5
Volume205
ISBN (Print)9781614994312
DOIs
StatePublished - 2014
Externally publishedYes
Event25th European Medical Informatics Conference, MIE 2014 - Istanbul, Turkey
Duration: Aug 31 2014Sep 3 2014

Publication series

NameStudies in Health Technology and Informatics
Volume205
ISSN (Print)09269630
ISSN (Electronic)18798365

Other

Other25th European Medical Informatics Conference, MIE 2014
CountryTurkey
CityIstanbul
Period8/31/149/3/14

Fingerprint

Vaccines
Electric network analyzers
Pattern recognition
Intussusception
Electric network analysis
Islands
Information Systems
Research Design
Visualization
Safety
Growth
Processing

Keywords

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

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management
  • Medicine(all)

Cite this

Botsis, T., Scott, J., Goud, R., Toman, P., Sutherland, A., & Ball, R. (2014). Novel Algorithms for Improved Pattern Recognition Using the US FDA Adverse Event Network Analyzer. In Studies in Health Technology and Informatics (Vol. 205, pp. 1178-1182). (Studies in Health Technology and Informatics; Vol. 205). IOS Press. https://doi.org/10.3233/978-1-61499-432-9-1178

Novel Algorithms for Improved Pattern Recognition Using the US FDA Adverse Event Network Analyzer. / Botsis, Taxiarchis; Scott, John; Goud, Ravi; Toman, Pamela; Sutherland, Andrea; Ball, Robert.

Studies in Health Technology and Informatics. Vol. 205 IOS Press, 2014. p. 1178-1182 (Studies in Health Technology and Informatics; Vol. 205).

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

Botsis, T, Scott, J, Goud, R, Toman, P, Sutherland, A & Ball, R 2014, Novel Algorithms for Improved Pattern Recognition Using the US FDA Adverse Event Network Analyzer. in Studies in Health Technology and Informatics. vol. 205, Studies in Health Technology and Informatics, vol. 205, IOS Press, pp. 1178-1182, 25th European Medical Informatics Conference, MIE 2014, Istanbul, Turkey, 8/31/14. https://doi.org/10.3233/978-1-61499-432-9-1178
Botsis T, Scott J, Goud R, Toman P, Sutherland A, Ball R. Novel Algorithms for Improved Pattern Recognition Using the US FDA Adverse Event Network Analyzer. In Studies in Health Technology and Informatics. Vol. 205. IOS Press. 2014. p. 1178-1182. (Studies in Health Technology and Informatics). https://doi.org/10.3233/978-1-61499-432-9-1178
Botsis, Taxiarchis ; Scott, John ; Goud, Ravi ; Toman, Pamela ; Sutherland, Andrea ; Ball, Robert. / Novel Algorithms for Improved Pattern Recognition Using the US FDA Adverse Event Network Analyzer. Studies in Health Technology and Informatics. Vol. 205 IOS Press, 2014. pp. 1178-1182 (Studies in Health Technology and Informatics).
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