Abstract
Many clinical domains involve the collection of different types and substantial numbers of data over time. This is especially true when monitoring chronic patients. It is highly desirable to assist human users (e.g., clinicians, researchers), or decision support applications (e.g., diagnosis, therapy, quality assessment), who need to interpret large amounts of time-oriented data by providing a useful method for querying not only raw data, but also its abstractions. A temporal-abstraction database mediator is a modular approach designed for answering abstract, time-oriented queries. Our approach focuses on the integration of multiple time-oriented data sources, domain-specific knowledge sources, and computation services. The mediator mediates abstract time-oriented queries from any application to the appropriate distributed components that can answer these queries. We describe a highly modular, distributed implementation of the temporal database mediator architecture in the medical domain, and provide insights regarding the effective implementation and application of such an architecture.
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Boaz, D., Shahar, Y. (2003). Idan: A Distributed Temporal-Abstraction Mediator for Medical Databases. In: Dojat, M., Keravnou, E.T., Barahona, P. (eds) Artificial Intelligence in Medicine. AIME 2003. Lecture Notes in Computer Science(), vol 2780. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39907-0_3
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DOI: https://doi.org/10.1007/978-3-540-39907-0_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20129-8
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