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Semantic Approach to Public Health Situation Awareness ---
Design and Methodology
Parsa Mirhaji, S. Lillibridge, R. Richesson, J. Zhang, J. Smith
University of Texas Health Science Center at Houston, Houston, Texas
Corresponding author: Parsa Mirhaji, University of Texas Health Science Center at Houston, Houston, Texas 77030. Telephone: 713-500-3157; Fax: 713-500-0370; E-mail; pmirhaji@uth.tmc.edu.
Abstract
Introduction: A public health situation awareness system is proposed that 1) uses explicit representation
of surveillance concepts based on the user's cognitive model, and 2) is optimized for efficacy of performance
and relevance to the process and task, rather than for ontic accuracy of syndrome definitions.
Objectives: The goal of this effort is to develop a prototype knowledge-based system that demonstrates the utility of knowledge-intensive approaches in 1) integrating heterogeneous information (e.g., patient triage data,
pharmacy sales data, and school absenteeism data); 2) eliminating the effects of incomplete and poor-quality surveillance data; 3) reducing uncertainty in syndrome and aberration detection; and 4) enabling visualization of complex
information structures in surveillance settings, particularly in the context of biologic terrorism preparedness.
Methods: For this approach, explicit domain knowledge is the foundation for interpreting public health data, as opposed to conventional systems for which statistical methods are central. The system uses the Resource
Definition Framework (i.e., a framework for representing information that enables machines and humans to communicate)
and expressive language (i.e., Web Ontology Language [OWL]) to explicate human knowledge into machine-interpretable and computable problem-solving modules that can guide users and computer systems in sifting through relevant data to detect outbreaks.
Results: A prototype knowledge-based system for early detection of outbreaks of influenza, which has a complex natural pattern and is a potential agent for biologic terrorism, is being developed. A model has been developed (using OWL ontology language) to enable case detection for respiratory illness syndromes caused by weaponized influenza. A knowledge-based system to integrate relevant health data from nine community hospitals has also been developed.
Conclusions: Preliminary data from this effort will evaluate the utility of knowledge-based approaches
in information integration, syndrome and aberration detection, information visualization, and cross-domain investigation of root causes of events.
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