Ontologies, Ontology Languages, and Semantic Interoperability -- Segue to Electronic Health Records (EHR)

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

Electronic Health Records (EHR) - Semantic Interoperability - Part 1

To press a suit means one thing to a tailor and another thing to a lawyer.

A free radical means one thing to a chemist but meant another thing to members of the House Un-American Activities Committee (HUAC) during the 1950’s.

And, medication for pain and pain medication don’t always mean the same thing. The controversy surrounding the recent death of Michael Jackson illustrates this last point.

In the examples above, as in clinical terminology, words can take on different meanings depending on factors like time or place (i.e., context).

Furthermore, clinicians and organizations use different clinical terms that mean the same thing. For example, the terms heart attack, myocardial infarction, and MI may mean the same thing to a cardiologist, but, to a computer, they are all different. There is a need to exchange clinical information consistently between different health care providers, care settings, researchers and others (semantic interoperability), and because medical information is recorded differently from place to place (on paper or electronically), a comprehensive, unified medical terminology system is needed as part of the information infrastructure.

Interoperability

Interoperability is the ability of two parties, either human or machine, to exchange data or information.

First, syntactic interoperability guarantees the exchange of the structure of the data, but carries no assurance that the meaning will be interpreted identically by all parties. Web pages built with HTML or XML are good examples of machine-to-machine syntactic interoperability because a properly structured page can be read by any machine with a Web browser. The meaning of the page to a particular machine may vary substantially; however, this is not usually considered a problem because the semantics of a page are meant to be interpreted by human viewers.

Next, human or semantic interoperability guarantees that the meaning of a structure is unambiguously exchanged between humans. Documents such as progress notes, referrals, consults, and others achieve semantic interoperability at a clinician-to-clinician level by relying on common medical vocabularies.

Finally, computable semantic interoperability requires that the meaning of data be unambiguously exchanged from machine to machine (as shown in the figure below). This does not necessarily mean that all machines need to process the received data the same way, but rather that each machine will make its processing decisions based on the same meaning.



Words and Meanings

The meanings of words change, sometimes rapidly. But a formal language such as used in an ontology -- a rigorous and exhaustive organization of some knowledge domain that is usually hierarchical and contains all the relevant entities and their relations -- can encode the meanings (semantics) of concepts in a form that does not change. In order to determine what is the meaning of a particular word (or term in a database, for example), it is necessary to label each fixed concept representation in an ontology with the word(s) or term(s) that may refer to that concept.

When multiple words refer to the same (fixed) concept, in language this is called synonymy; when one word is used to refer to more than one concept, that is called ambiguity. Ambiguity and synonymy are among the factors that make computer understanding of language very difficult. The use of words to refer to concepts (the meanings of the words used) is very sensitive to the context and the purpose of any use for many human-readable terms.

The use of ontologies in supporting semantic interoperability is to provide a fixed set of concepts whose meanings and relations are stable and can be agreed to by users. When a word used in some interoperability context changes its meaning, then to preserve interoperability it is necessary to change the pointer to the ontology element(s) that specifies the meaning of that word.

There are a number of tools for the programmatic handling (i.e., creating, querying, etc.) of ontologies. The visual representation of ontologies is an important contribution of these tools.



IBM Integrated ontology Development Toolkit (formerly named IBM Semantics Toolkit) is one of many toolkits designed for storage, manipulation, query, and inference of ontologies and corresponding instances.

An upcoming post will discuss the role and value of semantic technology in service-oriented architectures (SOA).