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 Publications Only the 5 most recent displayed
- DataStorm - An Ontology-driven Framework for Cloud-based Data Analytic Systems
- T. W. Wlodarczyk, C. Rong, B. Jia, L. Cocanu, C. I. Nyulas, M. A. Musen
- IEEE 6th World Congress on Services (SERVICES 2010), Miami, Florida, USA, IEEE, 123-127. Published in 2010
- An efficient approach to intelligent real-time monitoring using ontologies and Hadoop
- T. W. Wlodarczyk, C. Rong, C. I. Nyulas, M. A. Musen
- 2010 International Conference on High Performance Computing and Simulation (HPCS 2010), Caen, France, IEEE, 209-215. Published in 2010
- A Bayesian Network Model for Analysis of Detection Performance in Surveillance Systems
- M. Izadi, D. Buckeridge, A. Okhmatovskaia, S. W. Tu, C. I. Nyulas, M. J. O'Connor, M. A. Musen
- AMIA Annual Symposium, San Francisco, CA. Published in 2009
- 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, 24, 3. Published in 2009
- 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, 2, 573-577. Published in 2008