Enhancing Biosurveillance Specificity Using PraedicoTM, A Next Generation Application

Alireza Vahdatpour, Cynthia A. Lucero-Obusan, Chris Lee, Gina Oda, Patricia Schirmer, Anosh Mostaghimi, Farshid Sedghi, Payam Etminani, Mark Holodniy

Abstract


 We evaluated the specificity of Praedico Biosurveillance, a next generation biosurveillance application leveraging multiple detection algorithms, big data and machine learning, for VA outpatient syndromic surveillance alerting during the period of June 2014 thru May 2015, and compared it to the Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE). Praedicoâ„¢ Biosurveillance generated alerts were significantly lower compared to ESSENCE generated alerts across all major syndromic syndromes and demonstrated higher sensitivity to seasons (i.e., ILI activity in winter). Reducing alerting fatigue would enhance specificity of computer-generated alerts, promoting more usage and gradual improvement in the algorithm's output.


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DOI: https://doi.org/10.5210/ojphi.v8i1.6588



Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org