TY - GEN
T1 - Novel Algorithms for Improved Pattern Recognition Using the US FDA Adverse Event Network Analyzer
AU - Botsis, Taxiarchis
AU - Scott, John
AU - Goud, Ravi
AU - Toman, Pamela
AU - Sutherland, Andrea
AU - Ball, Robert
N1 - Publisher Copyright:
© 2014 European Federation for Medical Informatics and IOS Press.
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - Big Data
KW - Network Analysis
KW - Pattern Recognition
KW - Safety Surveillance
UR - http://www.scopus.com/inward/record.url?scp=84929515750&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84929515750&partnerID=8YFLogxK
U2 - 10.3233/978-1-61499-432-9-1178
DO - 10.3233/978-1-61499-432-9-1178
M3 - Conference contribution
C2 - 25160375
AN - SCOPUS:84929515750
T3 - Studies in Health Technology and Informatics
SP - 1178
EP - 1182
BT - e-Health - For Continuity of Care - Proceedings of MIE 2014
A2 - Pape-Haugaard, Louise
A2 - Seroussi Brigitte, Brigitte
A2 - Saka, Osman
A2 - Lovis, Christian
A2 - Hasman, Arie
A2 - Andersen, Stig Kjaer
PB - IOS Press
T2 - 25th European Medical Informatics Conference, MIE 2014
Y2 - 31 August 2014 through 3 September 2014
ER -