KMeD: a Knowledge-based Multimedia Medical Distributed Database System

Integration and interoperability of a multimedia medical distributed database system. A temporal evolutionary object-oriented data model and its query language for medical image management. Using type inference and induced rules to provide intensional answers. 53 Integration. Images from the UCLA Department of Radiological Sciences are stored as regular Unix les under a speciic predeened PACS format (PACS is the oocial repository of digitized images in the UCLA Center for Health Science). The PACS format includes not only the actual image data, but standard alphanumeric information such as the patient's name, ID, the date of the examination, and others. We have extended our installation of GemStone so that it is capable of reading PACS les directly, without further intervention by the user, then integrating the PACS data right into the KMeD object-oriented database. Users of our testbed can thus access PACS image les from any location in the network that has access to our GemStone server. Cooperative query support. Together with the radiology group and medical experts, we have been gathering the knowledge necessary to provide cooperative query answering capabilities. This knowledge is stored in GemStone for representing type abstraction hierarchies. The object-orientation of GemStone makes it a very natural implementation base for a type abstraction hierarchy. We have implemented the relaxation manager with fuller functionality. We are currently in the process of implementing the explanation system, knowledge editors, and facilities for associative query answering 5]. Hardware/software platform. We use a Unix platform with Sun SPARCstations to develop our system. One of these stations acts as a server for GemStone, our primary database platform, and is also used for actual software and user interface development. GemStone allows transparent concurrent access to the database server from remote nodes, and provides bridges to other commercial database systems such as the relational Sybase (this allows access to heterogeneous databases). 4. CONCLUSION Our eeort embodies a unique and powerful conceptualization of heterogeneous scientiic databases. Not only do we solve the merger and management of observed data from multiple sources with spatial and evolutionary constructs, but our system strives to aid the scientist in extracting the science from the data. We have developed a semantic, spatial, evolutionary data model to allow us to retrieve images by features and contents. We have also developed a knowledge-based cooperative query facility with control policies to answer imprecise queries and also provide additional relevant information even if it is not …