Abstract: Multimedia medical databases have accumulated large quantities of data and information about patients and their medical conditions. Patterns and relationships within this data could provide new knowledge for making better medical decisions. Unfortunately, few technologies have been developed and applied to discover and use this hidden knowledge. We are currently developing a next generation knowledge-based multimedia medical database, named MedBase, with advanced behaviors for data analysis and data fusion. As part of this R&D effort, a knowledge-rich data model is constructed to incorporate data mining techniques/tools to assist the building of medical knowledge bases, and to facilitate intelligent answering of users' investigative and knowledge queries in the database. Techniques such as data generalization, classification, clustering, semantic structures, and concept hierarchies, are used to acquire and represent both symbolic and spatial knowledge implicit in the database. With the availability of semantic structures, concept hierarchies and generalized knowledge, queries may be posed and answered at multiple levels of abstraction. In this article we provide a general description of the approaches and efforts undertaken so far in the MedBase project. !27
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