Enhancing Biosurveillance Specificity Using PraedicoTM, A Next Generation Application
PDF

How to Cite

Vahdatpour, A., Lucero-Obusan, C. A., Lee, C., Oda, G., Schirmer, P., Mostaghimi, A., Sedghi, F., Etminani, P., & Holodniy, M. (2016). Enhancing Biosurveillance Specificity Using PraedicoTM, A Next Generation Application. Online Journal of Public Health Informatics, 8(1). https://doi.org/10.5210/ojphi.v8i1.6588

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.

https://doi.org/10.5210/ojphi.v8i1.6588
PDF
Authors own copyright of their articles appearing in the Online Journal of Public Health Informatics. Readers may copy articles without permission of the copyright owner(s), as long as the author and OJPHI are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. Share-alike: when posting copies or adaptations of the work, release the work under the same license as the original. For any other use of articles, please contact the copyright owner. The journal/publisher is not responsible for subsequent uses of the work, including uses infringing the above license. It is the author's responsibility to bring an infringement action if so desired by the author.