AbstractLoosely structured documents can capture more relevant information about medical events than is possible using today’s popular databases. In order to realize the full potential of this increased information content, techniques will be required that go beyond the static mapping of stored data into a single, rigid data model. Through intelligent processing, loosely structured documents can become a rich source of detailed data about actual events that can support the wide variety of applications needed to run a health-care organization, document medical care or conduct research. Abstraction and indirection are the means by which dynamic data models and intelligent processing are introduced into database systems. A system designed around loosely structured documents can evolve gracefully while preserving the integrity of the stored data. The ability to identify and locate the information contained within documents offers new opportunities to exchange data that can replace more rigid standards of data interchange.
Learning from similarity and information extraction from structured documents
