title: Novel algorithms for improved pattern recognition using the US FDA Adverse Event Network Analyzer.
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.
authors.affiliation: Office of Biostatistics and Epidemiology, CBER, FDA, Rockville, MD, USA.
authors.forename: Taxiarchis
authors.initials: T
authors.lastname: Botsis
authors.affiliation: Office of Biostatistics and Epidemiology, CBER, FDA, Rockville, MD, USA.
authors.forename: John
authors.initials: J
authors.lastname: Scott
authors.affiliation: Office of Biostatistics and Epidemiology, CBER, FDA, Rockville, MD, USA.
authors.forename: Ravi
authors.initials: R
authors.lastname: Goud
authors.affiliation: Booz Allen Hamilton, McLean, VA, USA.
authors.forename: Pamela
authors.initials: P
authors.lastname: Toman
authors.affiliation: Office of Biostatistics and Epidemiology, CBER, FDA, Rockville, MD, USA.
authors.forename: Andrea
authors.initials: A
authors.lastname: Sutherland
authors.affiliation: Office of Biostatistics and Epidemiology, CBER, FDA, Rockville, MD, USA.
authors.forename: Robert
authors.initials: R
authors.lastname: Ball
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems / Q000458:organization & administration*; D000465:Algorithms; D001185:Artificial Intelligence; D057286:Electronic Health Records / Q000458:organization & administration*; D006801:Humans; D015994:Incidence; D007443:Intussusception / Q000453:epidemiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods; D022243:Rotavirus Vaccines / Q000627:therapeutic use*; D012680:Sensitivity and Specificity; D018571:Sentinel Surveillance; D014481:United States / Q000453:epidemiology; D014486:United States Food and Drug Administration
chemical_list: D022243:Rotavirus Vaccines