Public health situation awareness: toward a semantic approach
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Introduction: We propose a knowledge-based public health situation awareness system. The basis for this system is an explicit representation of public health situation awareness concepts and their interrelationships. This representation is based upon the users' (public health decision makers) cognitive model of the world, and optimized towards the efficacy of performance and relevance to the public health situation awareness processes and tasks. In our approach, explicit domain knowledge is the foundation for interpretation of public health data, as apposed to conventional systems where the statistical methods are the essence of the processes. Objectives: To develop a prototype knowledge-based system for public health situation awareness and to demonstrate the utility of knowledge intensive approaches in integration of heterogeneous information, eliminating the effects of incomplete and poor quality surveillance data, uncertainty in syndrome and aberration detection and visualization of complex information structures in public health surveillance settings, particularly in the context of bioterrorism (BT) preparedness. Methods: The system employs the Resource Definition Framework (RDF) and additional layers of more expressive languages to explicate the knowledge of domain experts into machine interpretable and computable problem-solving modules that can then guide users and computer systems in sifting through the most "relevant" data for syndrome and outbreak detection and investigation of root cause of the event. Results: The Center for Biosecurity and Public Health Informatics Research is developing a prototype knowledge-based system around influenza, which has complex natural disease patterns, many public health implications, and is a potential agent for bioterrorism. Conclusions: The preliminary data from this effort may demonstrate superior performance in information integration, syndrome and aberration detection, information access through information visualization, and cross-domain investigation of the root causes of public health events.
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Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2004