Skip to main content
SpringerLink
Log in

Associating Clinical Archetypes Through UMLS Metathesaurus Term Clusters

  • Original Paper
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

Clinical archetypes are modular definitions of clinical data, expressed using standard or open constraint-based data models as the CEN EN13606 and openEHR. There is an increasing archetype specification activity that raises the need for techniques to associate archetypes to support better management and user navigation in archetype repositories. This paper reports on a computational technique to generate tentative archetype associations by mapping them through term clusters obtained from the UMLS Metathesaurus. The terms are used to build a bipartite graph model and graph connectivity measures can be used for deriving associations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. http://www.openehr.org/

  2. http://umlsinfo.nlm.nih.gov

  3. Elements in the openEHR Reference Model are in Courier font from here on.

  4. http://www.openehr.org/releases/1.0.2/

  5. http://www.ihtsdo.org/snomed-ct/

  6. Full ADL files for all the archetypes cited in this paper are available at http://openehr.org/knowledge/.

  7. Further details can be consulted in the UMLS Reference Manual, http://www.nlm.nih.gov/research/umls/documentation.html.

  8. http://umlsks.nlm.nih.gov/

  9. A bipartite graph, bigraph or two-mode network is a network where vertices are divided into two sets and vertices can only be related to vertices in the other set [21].

  10. http://jung.sourceforge.net/

  11. http://pajek.imfm.si/doku.php

  12. http://www.nlm.nih.gov/research/umls/knowledge_sources/metathesaurus/release/abbreviations.html

  13. http://www.openehr.org/svn/specification/TRUNK/publishing/architecture/am/adl1.5.pdf

  14. Specialised archetypes use a differential style of declaration (i.e. the contents of a specialised entity are expressed as differences with respect to the parent). The .adls standard file extension has been introduced for differential ADL files while the .adl files are retained for standalone archetypes.

References

  1. Bertaud, V., Lasbleiz, J., Mougin, F., Burgun, A., and Duvauferrier, R., A unified representation of findings in clinical radiology using the UMLS and DICOM. Int. J. Med. Inform. 77(9):621–629, 2008.

    Article  Google Scholar 

  2. Suebnukarn, S., Haddawy, P., and Rhienmora, P., A collaborative medical case authoring environment based on the UMLS. Journal of Biomedical Informatics 41(2):318–326, 2008.

    Article  Google Scholar 

  3. Marquet, G., Mosser, J., and Burgun, A., A method exploiting syntactic patterns and the UMLS semantics for aligning biomedical ontologies: The case of OBO disease ontologies. Int. J. Med. Inform., 76(3):S353–S361, 2007.

    Article  Google Scholar 

  4. Woods, J. W., Sneiderman, C. A., Hameed, K., Ackerman, M. J., and Hatton, C., Using UMLS metathesaurus concepts to describe medical images: Dermatology vocabulary. Comput. Biol. Med. 36(1):89–100, 2006.

    Article  Google Scholar 

  5. Brennan, P. F., and Aronson, A. R., Towards linking patients and clinical information: Detecting UMLS concepts in e-mail. Journal of Biomedical Informatics 36(4–5):334–341, 2003.

    Article  Google Scholar 

  6. Qamar, R., and Rector, A., Semantic mapping of clinical model data to biomedical terminologies to facilitate data interoperability. Presented at Healthcare Computing 2007 Conference, Harrogate, UK, 2007. Available: http://www.cs.man.ac.uk/~qamarr/papers/HealthcareComputing2007_Qamar.pdf. Accessed 9 August 2010.

  7. Bisbal, J., and Berry, D., Archetype alignment—a two-level driven semantic matching approach to interoperability in the clinical domain. Presented at International Conference on Health Informatics (HEALTHINF 2009), Porto, Portugal, 2009.

  8. Gresock, J. A., Finding combinatorial connections between concepts in the biomedical literature. M.S. thesis, Department of Computer Science, Faculty of the Virginia Polytechnic Institute and State University, Blacksburg, Virginia, U.S., 2007.

  9. Stroetmann, V. N., Kalra, D., Lewalle, P., Rodrigues, J. M., Stroetmann, K. A., Surjan, G., Ustun, B., Virtanen, M., and Zanstra, P. E., Semantic interoperability for better health and safer healthcare. Project report, European Commission. Office for Official Publications of the European Communities: Luxembourg, 2009.

  10. Schloeffel, P., Beale, T., Hayworth, G., Heard, S., and Leslie, H., The relationship between CEN 13606, HL7, and openEHR. In: Proceedings of HIC 2006 Bridging the Digital Divide: Clinician, Consumer and Computer. Seattle, Washington, USA, 2006.

  11. Michelsen, L., Pedersen, S. S., Tilma, H. B., and Andersen, S. K., Comparing different approaches to two-level modelling of electronic health records. Stud. Health Technol. Inform. 116:113–118, 2005.

    Google Scholar 

  12. Beale, T., Heard, S., Kalra, D., and Lloyd, D., The openEHR EHR information model, The openEHR Reference Model, The openEHR Foundation, rev. 5.1.0, 2007.

  13. Beale, T., Archetypes, constraint-based domain models for future-proof information systems. In: Proceedings of the OOPSLA 2002 Conference. pp. 16–32. Boston, Seattle, Washington, USA, 2002.

  14. Chen, R., and Klein, G., The openEHR Java reference implementation project. Stud. Health Technol. Inform., 129(pt. 1):58–62, 2007.

    Google Scholar 

  15. Bodenreider, O., The Unified Medical Language System (UMLS): Integrating biomedical terminology. Nucleic Acids Res. 32(database issue D267–D270), 2004.

    Article  Google Scholar 

  16. Lindberg, D. A., Humphreys, B. L., and McCray, A. T., The unified medical language system. Methods Inf. Med. 32(4):281–291, 1993.

    Google Scholar 

  17. Bangalore, A., Thorn, K. E., Tilley, C., and Peters, L., The UMLS knowledge source server: An object model for delivering UMLS data. In: Proceedings of the AMIA 2003 Symposium. pp. 51–55, 2003.

  18. McCray, A. T., Srinivasan, S., and Browne, A. C., Lexical methods for managing variation in biomedical terminologies. In: Proceedings of the Annual Symposium on Computer Applications in Medical Care. pp. 235–239, 1994.

  19. Browne, A. C., Divita, G., Lu, C., McCreedy, L., and Nace, D., Lexical systems: A report to the Board of Scientific Counselors, Lister Hill National Center for Biomedical Communications, National Library of Medicine. Tech. Rep. LHNCBC-TR-2003-003, 2003.

  20. Browne, A. C., McCray, A. T., and Srinivasan, S., The SPECIALIST LEXICON, Lister Hill National Center for Biomedical Communications, National Library of Medicine: Bethesda, MD, 2000.

    Google Scholar 

  21. de Nooy, W., Mrvar, A., and Batagelj, V., Exploratory social network analysis with Pajek. New York: Cambridge University Press, 2005.

    Google Scholar 

  22. O’Madadhain, J., Fisher, D., White, S., and Boey, Y. B., The JUNG (Java Universal Network/Graph) Framework, University of California, Irvine, CA. Tech Rep. UCI-ICS 03-17, 2003.

  23. Fruchterman, T., and Reingold, E., Graph drawing by force-directed placement. Softw. Pract. Exp. 21(11):1129–1164, 1991.

    Article  Google Scholar 

  24. Batagelj, V., and Mrvar, A., Pajek: Analysis and visualization of large networks. In: Graph Drawing Software. pp. 77–103. Springer, 2003.

  25. Lamping, J., and Rao, R., The hyperbolic browser: A focus+ context technique for visualizing large hierarchies. J. Vis. Lang. Comput. 6(4), 1995.

Download references

Acknowledgements

This work has been supported by the project “Historia Clínica Inteligente para la seguridad del Paciente/Intelligent Clinical Records for Patient Safety” (CISEP), code FIT-350301-2007-18, funded by the Spanish Ministry of Science and Technology.

Author information

Authors and Affiliations

  1. Information Engineering Research Unit, Computer Science Department, University of Alcalá, Alcalá, Spain

    Leonardo Lezcano, Salvador Sánchez-Alonso & Miguel-Angel Sicilia

Authors
  1. Leonardo Lezcano
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Salvador Sánchez-Alonso
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Miguel-Angel Sicilia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Miguel-Angel Sicilia.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lezcano, L., Sánchez-Alonso, S. & Sicilia, MA. Associating Clinical Archetypes Through UMLS Metathesaurus Term Clusters. J Med Syst 36, 1249–1258 (2012). https://doi.org/10.1007/s10916-010-9586-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10916-010-9586-9

Keywords

Search

Navigation