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Issue No.05 - September/October (2005 vol.20)
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
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, September/October 2005, doi:10.1109/MIS.2005.91
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