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Issue No. 05 - September/October (2005 vol. 20)
ISSN: 1541-1672
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.<p>This article is part of a special issue on Homeland Security.</p>
ontologies, knowledge modeling, knowledge-based systems, ontology mapping, data integration, problem-solving methods, syndromic surveillance, bioterrorism tracking, alerting, and analysis, disease prevention and detection

M. Crub?zy, M. A. Musen, M. O'Connor, D. L. Buckeridge and Z. Pincus, "Ontology-Centered Syndromic Surveillance for Bioterrorism," in IEEE Intelligent Systems, vol. 20, no. , pp. 26-35, 2005.
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