Exploiting Fast Classification of SNOMED CT for Query and Integration of Health Data

3rd International Conference on Knowledge Representation in Medicine

计算机科学;图书情报档案学

Michael J. Lawley

Others  :  http://CEUR-WS.org/Vol-410/Paper02.pdf
PID  :  25208

来源: CEUR

【 摘 要 】

By constructing local extensions to SNOMED we aim to enrich existing medical and related data stores, simplify the expression of complex queries, and establish a foundation for semantic integration of data from multiple sources. Specifically, a local extension can be constructed from the controlled vocabulary(ies) used in the medical data. In combination with SNOMED, this local extension makes explicit the implicit semantics of the terms in the controlled vocabulary. By using SNOMED as a base ontology we can exploit the existing knowledge encoded in it and simplify the task of reifying the implicit semantics of the controlled vocabulary. Queries can now be formulated using the relationships encoded in the extended SNOMED rather than embedding them ad-hoc into the query itself. Additionally, SNOMED can then act as a common point of integration, providing a shared set of concepts for querying across multiple data sets. Key to practical construction of a local extension to SNOMED is appropriate tool support including the ability to compute subsumption relationships very quickly. Our implementation of the polynomial algorithm for ει+ in Java is able to classify SNOMED in under 1 minute.