title: Can network analysis improve pattern recognition among adverse events following immunization reported to VAERS?
abstract: Current methods of statistical data mining are limited in their ability to facilitate the identification of patterns of potential clinical interest from spontaneous reporting systems of medical product adverse events (AEs). Network analysis (NA) allows for simultaneous representation of complex connections among the key elements of such a system. The Vaccine Adverse Event Reporting System (VAERS) can be represented as a network of 6,428 nodes (74 vaccines and 6,354 AEs) with more than 1.4 million interlinkages. VAERS has the characteristics of a "scale-free" network, with certain vaccines and AEs acting as "hubs" in the network. Known safety signals were visualized using NA methods, including hub identification. NA offers a complementary approach to current statistical data-mining techniques for visualizing multidimensional patterns, providing a structural framework for evaluating AE data.
authors.affiliation: Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, US Food and Drug Administration, Rockville, Maryland, USA.
authors.forename: R
authors.initials: R
authors.lastname: Ball
authors.forename: T
authors.initials: T
authors.lastname: Botsis
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems / Q000706:statistics & numerical data*; D003627:Data Interpretation, Statistical; D057225:Data Mining / Q000379:methods*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D014612:Vaccines / Q000009:adverse effects*
chemical_list: D014612:Vaccines