A knowledge-based approach to temporal abstraction of clinical data for disease surveillance

Mark A. Musen, Martin J. O’Connor, David L. Buckeridge, Yuval Shahar, Kurt A. Henry

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Automated surveillance for disease detection requires the integration of many kinds of data. The clinical information systems in hospitals, clinics, and emergency rooms that record patient findings and conditions are a fruitful source of information that may suggest the presence of incipient epidemics in a timely manner. Analysis of data from electronic patient record systems requires methods that can integrate both numeric and nonnumeric information, and that can use the clinical context to aid in data interpretation. Techniques from artificial intelligence that can take advantage of clinical knowledge that has been encoded in machine-processable form are particularly well suited for disease surveillance. We describe the knowledge-based temporal abstraction (KBTA) method, which we believe will be particularly useful in performing
automated surveillance of clinical data to detect occult acts of bioterrorism.
Original languageEnglish
Title of host publicationProceedings of the NATO Human Factors in Medicine Symposium on Operational Issues in Chemical and Biological Defense, Estoril, Portugal
StatePublished - 2001
Externally publishedYes


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