@inproceedings{222bc83ab3df44299602ae98ff5fe466,
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.",
keywords = "Big Data, Network Analysis, Pattern Recognition, Safety Surveillance",
author = "Taxiarchis Botsis and John Scott and Ravi Goud and Pamela Toman and Andrea Sutherland and Robert Ball",
year = "2014",
doi = "10.3233/978-1-61499-432-9-1178",
language = "English (US)",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press",
pages = "1178--1182",
editor = "Louise Pape-Haugaard and {Seroussi Brigitte}, Brigitte and Osman Saka and Christian Lovis and Arie Hasman and Andersen, {Stig Kjaer}",
booktitle = "e-Health - For Continuity of Care - Proceedings of MIE 2014",
note = "25th European Medical Informatics Conference, MIE 2014 ; Conference date: 31-08-2014 Through 03-09-2014",
}