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To assess the degree to which information retrieved from a biomedical database can augment personal knowledge in addressing novel problems, and how the ability to retrieve information evolves over time.

CDC WONDER is an information management architecture designed for public health. It provides access to information and communications without the user's needing to know the location of data or communication pathways and mechanisms. CDC WONDER users have access to extractions from some 40 databases; electronic mail (e-mail); and surveillance data processing. System components include the Remote Client, the Communications Server, the Queue Managers, and Data Servers and Process Servers. The [...]

To examine the influences of situational and model factors on the accuracy of Bayesian learning systems.

Development of methods for building concept models to support structured data entry and image retrieval in chest radiography.

Develop a representational schema for clinical concepts and apply it to the task of encoding radiology reports of the chest.

The Canon Group is an informal organization of medical informatics researchers who are working on the problem of developing a "deeper" representation formalism for use in exchanging data and developing applications. Individuals in the group represent experts in such areas as knowledge representation and computational linguistics, as well as in a variety of medical subdisciplines. All share the view that current mechanisms for the characterization of medical phenomena are either [...]