Knowledge-Based Syndromic Surveillance for Bioterrorism

AI Technologies for Homeland Security

Volume

Papers from the 2005 AAAI Spring Symposium

Contents

Syndromic surveillance requires the acquisition and analysis of data that may be suggestive of early epidemics in a community, long before there is any categorical evidence of unusual infection. These data are often heterogenous and often quite noisey. The processs of syndromic surveillance poses problems in data integration; in selection of appropriate reusable problem-solving methods, based on task features and on the nature of the data at hand; and in mapping integrated data to appropriate problem solvers. These are all tasks that have been studied carefully in the knowledge-based systems community for many years. We demonstrate how a software architecture that suppports knoweldge-based data integrationa and problem solving facilitates many aspects of the syndromic-surveillance task. In particular, we use reference ontologies for purposes of semantic integration and a parallelizable blackboard architecture for invocation of appropriate problem solving methods and for control of reasoning. We demonstrate our results in the context of a prototype system known as the Biological Spacio-Temporal Outbreak Reasoning Module (BioSTORM), which offers an end-to-end solution to the problem of syndromic surveillance.