Skip to main content
SpringerLink
Log in
Book cover

Conference on Artificial Intelligence in Medicine in Europe

AIME 1995: Artificial Intelligence in Medicine pp 15–28Cite as

Coordinating taxonomies: Key to re-usable concept representations

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 934))

Abstract

A unified controlled medical vocabulary has been cited as one of the grand challenges facing Medical Informatics. We would restate this challenge as ‘achieving a reusable and application-independent representation of medical concepts.’ Achieving a reusable representation of medical concepts is a pre-requisite for meeting two key strategic goals of the next decade of the development in medical informatics: interoperability and cumulative development. A key strategy for achieving re-usability is to separate concepts into their component parts, organise those parts in nearly pure hierarchies, and then recombine into composite representations which can be classified flexibly and automatically. This paper explores the means and consequences of this strategy as implemented in the GALEN project. It discusses both the strengths — providing greater detail, greater computer support, and avoiding many arguments which are endemic in discussions of classification systems — and the limitations intrinsic in such a formal approach.

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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Masarie Jr F, Miller R, Bouhaddou O, Giuse N, Warner H. An interlingua for electronic interchange of medical information: using frames to map between clinical vocabularies. Computers in Biomedical Research 1991;24(4):379–400.

    Article  Google Scholar 

  2. Evans DA, Cimino J, Hersh WR, Huff SM, Bell DS, The Canon Group. Position Statement: Towards a Medical Concept Representation Language. Journal of the American Medical Informatics Association 1994;1(3):207–217.

    PubMed  Google Scholar 

  3. Campbell KE, Das AK, Musen MA. A logical foundation for representation of clinical data. JAMIA 1994;1(3):218–232.

    PubMed  Google Scholar 

  4. Cimino J. Controlled Medical Vocabulary Construction: Methods from the Canon Group. Journal of the American Medical Informatics Association 1994;1(3):296–197.

    PubMed  Google Scholar 

  5. Bergamachi S, Sartori C. On taxonomic reasoning in conceptual design. ACM Transactions on Database Systems 1992;17(3):385–421.

    Article  Google Scholar 

  6. Maida AS. Knowledge representation requirements for description-based communication. in: Nebel B, Rich C, Swartout W, (ed). Third International Conference on Principles of Knowledge Representation and Reasoning, KR '92. Cambridge MA: Morgan Kaufmann, 1992: 232–243.

    Google Scholar 

  7. Borgida A. A new look at the foundations and utility of Description Logics (or Terminology Logics are not just for the Flightless Birds). Internal report. Rutgers University Department of Computer Science, 1992

    Google Scholar 

  8. Rector A, Gangemi A, Galeazzi E, Glowinski A, Rossi-Mori A. The GALEN CORE Model Schemata for Anatomy: Towards a re-usable application-independent model of medical concepts. in: Barahona P, Veloso M, Bryant J, (ed). Twelfth International Congress of the European Federation for Medical Informatics, MIE-94. Lisbon, Portugal, 1994: 229–233.

    Google Scholar 

  9. Rector A, Nowlan W, Glowinski A. Goals for Concept Representation in the GALEN project. 17th Annual Symposium on Computer Applications in Medical Care (SCAMC-93). McGraw Hill, 1993: 414–418.

    Google Scholar 

  10. Rector A, Nowlan W. The GALEN Representation and Integration Language (GRAIL) Kernel, Version 1. The GALEN Consortium for the EC AIM Programme. (Available from Medical Informatics Group, University of Manchester), 1993

    Google Scholar 

  11. Brachman R, Jb S. An overview of the KL-ONE knowledge representation system. Cognitive Science 1985;9(2):171–216.

    Article  Google Scholar 

  12. Borgida A, Brachman RJ, McGuiness DL, Resnick LA. CLASSIC: A Structural Data Model for Objects. ACM SIGMOD International Conference on Management of Data. ACM, 1989: 58–67.

    Google Scholar 

  13. Nebel B. Computational Complexity of Terminological Reasoning in Back. Artificial Intelligence 1988;34:371–383.

    MathSciNet  Google Scholar 

  14. Sowa J. Conceptual Structures: Knowledge Representation in Mind and Machine. New York: John Wiley & Sons, 1985

    Google Scholar 

  15. Bernauer J, Goldberg H. Compositional classification based on conceptual graphs. Artificial Intelligence in Medicine Europe (AIME-93). Munich:, 1993:.

    Google Scholar 

  16. Tuttle MS. The position of the canon group: A reality check. Journal of the American Medical Informatics Association 1994;1(3):298–299.

    PubMed  Google Scholar 

  17. MacGregor R. The evolving technology of classification-based knowledge representation systems. in: Sowa J, ed. Principles of Semantic Networks: Explorations in the representation of knowledge. San Mateo, CA: Morgan Kaufmann, 1991: 385–400.

    Google Scholar 

  18. Brachman R, Fikes R, Levesque H. An essential hybrid reasoning system; knowledge and symbol level accounts of KRYPTON. International Joint Conference on Artificial Intelligence (IJCAI-85). Morgan Kaufman, 1985: 532–539.

    Google Scholar 

  19. Nowlan W, Rector A. Medical Knowledge Representation and Predictive Data Entry. in: Stefanelli M, Hasman A, Fiesch M, Talmon J, (ed). Artificial Intelligence in Medicine Europe (AIME-91). Maastricht: Springer-Verlag, 1991: 105–116.

    Google Scholar 

  20. Nowlan W, Rector A, Kay S, Horan B, Wilson A. A Patient Care Workstation Based on a User Centred Design and a Formal Theory of Medical Terminology: PEN&PAD and the SMK Formalism. in: Clayton P, (ed). Fifteenth Annual Symposium on Computer Applications in Medical Care. SCAMC-91. Washington DC: McGraw-Hill, Inc, 1991: 855–857.

    Google Scholar 

  21. Horan B, Rector A, Sneath E, et al. Supporting a Humanly Impossible Task: The Clinical Human-Computer Environment,. in: Diaper D, (ed). Interact 90. Elsevier Science Publishers, B.V.North-Holland, 1990: 247–252.

    Google Scholar 

  22. Solomon W, Heathfield H. Conceptual modelling used to represent drug interactions. in: Barahona P, Veloso M, Bryant J, (ed). Twelfth International Congress of the European Federation for Medical Informatics, MIE-94. Lisbon, Portugal:, 1994: 186–190.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Medical Informatics Group, Department of Computer Science, University of Manchester, M13 9PL, Manchester, England

    A. L. Rector

Authors
  1. A. L. Rector
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Pedro Barahona Mario Stefanelli Jeremy Wyatt

Rights and permissions

Reprints and permissions

Copyright information

© 1995 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Rector, A.L. (1995). Coordinating taxonomies: Key to re-usable concept representations. In: Barahona, P., Stefanelli, M., Wyatt, J. (eds) Artificial Intelligence in Medicine. AIME 1995. Lecture Notes in Computer Science, vol 934. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60025-6_122

Download citation

  • DOI: https://doi.org/10.1007/3-540-60025-6_122

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60025-1

  • Online ISBN: 978-3-540-49407-2

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation