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BioSTORM

Project Description

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 now 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 (Biological Spatio-Temporal Outbreak Reasoning Module), an experimental system that offers an end-to-end solution to syndromic surveillance.

View Project's Website: http://biostorm.stanford.edu/


Related People

Related Publications Only the 5 most recent displayed

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BMIR-2009-1383
A Bayesian Network Model for Analysis of Detection Performance in Surveillance Systems
M. Izadi, D. Buckeridge, A. Okhmatovskaia, S. W. Tu, M. J. O'Connor, C. I. Nyulas, M. A. Musen
AMIA Annual Symposium, San Francisco, CA. In Press in 2009
BMIR-2009-1358
Software-Engineering Challenges of Building and Deploying Reusable Problem Solvers
M. J. O'Connor, C. I. Nyulas, A. Okhmatovskaia, D. Buckeridge, S. W. Tu, M. A. Musen
Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 23, 3. In Press in 2009
BMIR-2008-1329
An Ontology-Driven Framework for Deploying JADE Agent Systems
C. I. Nyulas, M. J. O'Connor, S. W. Tu, A. Okhmatovskaia, D. Buckeridge, M. A. Musen
IEEE/WIC/ACM International Conference on Intelligent Agent Technology, Sydney, Australia. In Press in 2008
BMIR-2008-1322
Understanding Detection Performance in Public Health Surveillance: Modeling Aberrancy-Detection Algorithms
D. Buckeridge, A. Okhmatovskaia, S. W. Tu, M. J. O'Connor, C. I. Nyulas, M. A. Musen
Journal of the American Medical Informatics Association, 15, 6, 760-769. Published in 2008
BMIR-2008-1321
Predicting Outbreak Detection in Public Health Surveillance: Quantitative Analysis to Enable Evidence-Based Method Selection
D. Buckeridge, A. Okhmatovskaia, S. W. Tu, M. J. O'Connor, C. I. Nyulas, M. A. Musen
AMIA Annual Symposium, Washington, DC. Published in 2008

Stanford School of Medicine