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Ontology-Centered Syndromic Surveillance for Bioterrorism
September/October 2005 (vol. 20 no. 5)
pp. 26-35
Monica Crub?zy, Stanford University
Martin O'Connor, Stanford University
David L. Buckeridge, Stanford University and the VA Palo Alto Health Care System
Zachary Pincus, Stanford University
Mark A. Musen, Stanford University
Syndromic surveillance requires acquiring and analyzing data that might suggest early epidemics in a community, long before there's categorical evidence of unusual infection. These data are often heterogeneous and noisy, and public health analysts must interpret them with a combination of analytic methods. Syndromic surveillance thus involves integrating data, configuring problem-solving strategies, and mapping integrated data to appropriate methods. The knowledge-based systems community has studied these tasks for years. We present a software architecture that supports knowledge-based data integration and problem solving, thereby facilitating many syndromic surveillance aspects. Central to our approach, a set of reference ontologies supports semantic integration, and a parallelizable blackboard architecture implements invocation of appropriate problem-solving methods and reasoning control. We demonstrate our approach with BioStorm, an experimental system that offers an end-to-end solution to syndromic surveillance.

This article is part of a special issue on Homeland Security.

Index Terms:
ontologies, knowledge modeling, knowledge-based systems, ontology mapping, data integration, problem-solving methods, syndromic surveillance, bioterrorism tracking, alerting, and analysis, disease prevention and detection
Monica Crub?zy, Martin O'Connor, David L. Buckeridge, Zachary Pincus, Mark A. Musen, "Ontology-Centered Syndromic Surveillance for Bioterrorism," IEEE Intelligent Systems, vol. 20, no. 5, pp. 26-35, Sept.-Oct. 2005, doi:10.1109/MIS.2005.91
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