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Can Network Analysis Improve Pattern Recognition Among Adverse Events Following Immunization Reported to VAERS?
Author(s) -
Ball R,
Botsis T
Publication year - 2011
Publication title -
clinical pharmacology and therapeutics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.941
H-Index - 188
eISSN - 1532-6535
pISSN - 0009-9236
DOI - 10.1038/clpt.2011.119
Subject(s) - adverse event reporting system , identification (biology) , key (lock) , computer science , adverse effect , statistical analysis , representation (politics) , data mining , medicine , statistics , pharmacology , mathematics , computer security , biology , botany , politics , political science , law
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. Clinical Pharmacology & Therapeutics (2011) 90 2, 271–278. doi: 10.1038/clpt.2011.119