Advertisement

How Ontologies Can Improve Semantic Interoperability in Health Care

  • Stefan Schulz
  • Catalina Martínez-Costa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8268)

Abstract

The main rationale of biomedical terminologies and formalized clinical information models is to provide semantic standards to improve the exchange of meaningful clinical information. Whereas terminologies should express context-independent meanings of domain terms, information models are built to represent the situational and epistemic contexts in which domain terms are used. In practice, semantic interoperability is encumbered by a plurality of different encodings of the same piece of clinical information. The same meaning can be represented by single codes in different terminologies, pre- and postcoordinated expressions in the same terminology, as well as by different combinations of (partly overlapping) terminologies and information models.

Formal ontologies can support the automatically recognition and processing of such heterogeneous but isosemantic expressions. In the SemanticHealthNet Network of Excellence a semantic framework is being built which addresses the goal of semantic interoperability by proposing a generalized methodology of transforming existing resources into “semantically enhanced” ones. The semantic enhancements consist in annotations as OWL axioms which commit to an upper-level ontology that provides categories, relations, and constraints for both domain entities and informational entities. Prospects and the challenges of this approach – particularly human and computational limitations – are discussed.

Keywords

Formal Ontology Medical Terminologies Health Care Standards 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Heflin, J., Hendler, J.: Semantic Interoperability on the Web (2000), http://www.cs.umd.edu/projects/plus/SHOE/pubs/extreme2000.pdf (last accessed July 17, 2013)
  2. 2.
    Quine, W.V.: On what there is. In: Gibson, R. (ed.) Quintessence-Basic Readings from the Philosophy of W. V. Quine. Belknap Press, Cambridge (2004)Google Scholar
  3. 3.
    Bodenreider, O., Smith, B., Burgun, A.: The Ontology‐Epistemology Divide: A Case Study in Medical Terminology. In: Proceedings of FOIS 2004, pp. 185–195. IOS Press, Amsterdam (2004)Google Scholar
  4. 4.
    Hofweber, T.: Logic and Ontology. In: Zalta, E.N. (ed.) The Stanford Encyclopedia of Philosophy, 2013th edn. (Spring 2013), http://plato.stanford.edu/archives/spr2013/entries/logic-ontology/ (last accessed July 17, 2013)
  5. 5.
    United States National Library of Medicine (NLM). Medical Subject Headings, MeSH (2013), http://www.nlm.nih.gov/mesh (last accessed July 17, 2013)
  6. 6.
    Smith, B., Ashburner, M., Rosse, C., Bard, J., Bug, W., Ceusters, W., Goldberg, L.J., Eilbeck, K., Ireland, A., Mungall, C.J.: OBI Consortium. In: Leontis, N., Rocca-Serra, P., Ruttenberg, A., Sansone, S.A., Scheuermann, R.H., Shah, N., Whetzel, P.L., Lewis, S. (eds.) The OBO Foundry: Coordinated Evolution of Ontologies to Support Biomedical Data Integration. Nature Biotechnology, vol. 25(11), pp. 1251–1255 (November 2007)Google Scholar
  7. 7.
    Systematized Nomenclature of Medicine - Clinical Terms, SNOMED CT (2008), http://www.ihtsdo.org/snomed-ct (last accessed July 17, 2013)
  8. 8.
    World Health Organization (WHO). International Classification of Diseases (ICD) (2013), http://www.who.int/classifications/icd (last accessed July 17, 2013)
  9. 9.
    OpenEHR. An open domain-driven platform for developing flexible e-health systems, http://www.openehr.org (last accessed July 17, 2013)
  10. 10.
    En13606 Association, http://www.en13606.org/ (last accessed July 17, 2013)
  11. 11.
    Health Level Seven International, http://www.hl7.org/ (last accessed July 17, 2013)
  12. 12.
    SemanticHealthNet Network of Excellence, http://www.semantichealthnet.eu/ (last accessed July 17, 2013)
  13. 13.
    Baader, F., Calvanese, D., McGuinness, D.L., Nardi, D., Patel-Schneider, P.F.: The Description Logic Handbook. Theory, Implementation, and Applications, 2nd edn. Cambridge University Press, Cambridge (2007)CrossRefzbMATHGoogle Scholar
  14. 14.
    W3C OWL working group. OWL 2 Web Ontology Language, Document Overview. W3C Recommendation (December 11, 2012), http://www.w3.org/TR/owl2-overview/ (last accessed July 17, 2013)
  15. 15.
    Schulz, S., Jansen, L.: Formal ontologies in biomedical knowledge representation. Yearbook of Medical Informatics (2013)Google Scholar
  16. 16.
    Cohen, S.M.: Aristotle’s metaphysics. Stanford Encyclopedia of Philosophy (2012), http://plato.stanford.edu/entries/aristotle-metaphysics/ (last accessed July 17, 2013)
  17. 17.
    Schober, D., Smith, B., Lewis, S.E., Kusnierczyk, W., Lomax, J., Mungall, C., Taylor, C.F., Rocca-Serra, P., Sansone, S.A.: Survey-based naming conventions for use in OBO Foundry ontology development. BMC Bioinformatics 10, 125 (2009)CrossRefGoogle Scholar
  18. 18.
    Seddig-Raufie, D., Jansen, L., Schober, D., Boeker, M., Grewe, N., Schulz, S.: Proposed actions are no actions: re-modeling an ontology design pattern with a realist top-level ontology. J. Biomed. Semantics 3(suppl. 2), S2 (2012)Google Scholar
  19. 19.
    Schulz, S., Boeker, M.: BioTopLite: An Upper Level Ontology for the Life Sciences. In: Evolution, Design and Application. Workshop on Ontologies and Data in Life Sciences, Koblenz, Germany, September 19-20 (2013)Google Scholar
  20. 20.
    TermInfo Project, http://www.hl7.org/special/committees/terminfo/ (last accessed July 17, 2013)
  21. 21.
    Clinical Information Modeling Initiative (CIMI), http://informatics.mayo.edu/CIMI/ (last accessed July 17, 2013)
  22. 22.
    Clinical Element Model (CEM), http://informatics.mayo.edu/sharp/ (last accessed July 17, 2013)
  23. 23.
    Logical Record Architecture (LRA), http://www.connectingforhealth.nhs.uk/systemsandservices/data/lra (last accessed July 17, 2013)
  24. 24.
    Detailed Clinical Models (DCMs), http://www.detailedclinicalmodels.nl/ (last accessed July 17, 2013)
  25. 25.
    Martínez Costa, C., Schulz, S.: Ontology-based reinterpretation of the SNOMED CT context model. In: Fourth International Conference in Biomedical Ontologies (ICBO 2013), Montreal, Canada, July 6-9 (2013)Google Scholar
  26. 26.
    SPARQL Query Language For RDF. W3C Recommendation (January 15, 2008), http://www.w3.org/TR/rdf-sparql-query/ (last accessed July 17, 2013)

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Stefan Schulz
    • 1
  • Catalina Martínez-Costa
    • 1
  1. 1.Institute for Medical Informatics, Statistics and DocumentationMedical University of GrazAustria

Personalised recommendations