This post serves as a transition between my previous one on OWL and ontologies and my upcoming ones on electronic health records (EHR) - past, present and future.
OWL is the latest standard in ontology languages from the World Wide Web Consortium (W3C) - it is built on top of RDF (i.e., OWL semantically extends RDF).
These two languages are explained in
http://www.co-ode.org/resources/tutorials/intro/slides/OWLFoundationsSlides.pdf
As an example of an ontology that's central to building an interoperable EHR system, I'll cite the Systematized Nomenclature of Medicine (SNOMED) ontology, which is used in more than 50 countries around the world:
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The Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) Ontology includes a Core terminology of over 364,000 health care concepts with unique meanings and formal logic—based definitions organized into hierarchies. As of January 2005, the fully populated table with unique descriptions for each concept contained more than 984,000 descriptions. Approximately 1.45 million semantic relationships exist to enable reliability and consistency of data retrieval. SNOMED CT is available in English, Spanish and German language editions.
What is its structure?
Core content includes the concepts table, descriptions table, relationships table, history table, an ICD-9-CM mapping, and the Technical Reference Guide. And, it can map to other medical terminologies and classification systems already in use.
SNOMED is meant to be complementary to LOINC (Logical Observations Identifiers, Names, Codes), another clinical terminology important for laboratory test orders and results.
See http://www.ihtsdo.org/ for a good deal more information on SNOMED.
For advanced IT readers
The DL that SNOMED uses is much less expressive than OWL. The result is, even though you can mechanically translate SNOMED into OWL, the resulting OWL ontology will be very unlike anything an OWL author would create starting from scratch, and might also be a challenge to classify successfully under an OWL reasoner without a lot of manual editing.
Furthermore, even at that point it would also be of limited value in supporting reasoning over OWL instances, as many kinds of assertions that would be routine in an OWL ontology (like disjoints, explicit domain/range constraints, etc.) do not exist in native SNOMED and would have to be created.
One opinion holds that creating your own OWL ontology and using SNOMED as a mapping target leverages SNOMED in a more useful way for most conceivable applications.
Finally, for a good deal more on the topics covered so far, consider
Clinical Decision Support Systems
Theory and Practice
Series: Health Informatics
Berner, Eta S. (Ed.)
2nd ed., 2007
ISBN: 978-0-387-33914-6
