Skip to main content

Analyzing Differences in Operational Disease Definitions Using Ontological Modeling

  • Conference paper
Artificial Intelligence in Medicine (AIME 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4594))

Included in the following conference series:

Abstract

In medicine, there are many diseases which cannot be precisely characterized but are considered as natural kinds. In the communication between health care professionals, this is generally not problematic. In biomedical research, however, crisp definitions are required to unambiguously distinguish patients with and without the disease. In practice, this results in different operational definitions being in use for a single disease. This paper presents an approach to compare different operational definitions of a single disease using ontological modeling. The approach is illustrated with a case-study in the area of severe sepsis.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Peelen, L., De Keizer, N., Peek, N., De Jonge, E., Bosman, R., Scheffer, G.: Influence of entry criteria on mortality risk and number of eligible patients in recent studies on severe sepsis. Crit. Care Med. 33, 2178–2183 (2005)

    Article  Google Scholar 

  2. Bernard, G., Vincent, J.L., Laterre, P.F., et al.: Efficacy and safety of recombinant human activated protein C for severe sepsis. N Engl. J Med. 344, 699–709 (2001)

    Article  Google Scholar 

  3. Warren, B., Eid, A., Singer, P., et al.: High-dose antithrombin III in severe sepsis. A randomized controlled trial. JAMA 286, 1869–1878 (2001)

    Google Scholar 

  4. Baader, F., Calvanese, D., McGuinness, D., Nardi, D., Patel-Schneider, P. (eds.): The DL Handbook. Cambridge University Press, Cambridge (2003)

    Google Scholar 

  5. Peelen, L., Klein, M., Schlobach, S., De Keizer, N., Peek, N.: Analyzing differences in operational disease definitions using ontological modeling with an application in severe sepsis. Technical Report 2007-02, Dept. of Medical Informatics, Academic Medical Center - Universiteit van Amsterdam (2007)

    Google Scholar 

  6. Modgil, S., Hammond, P.: Decision support for clinical trial design. Artificial Intelligence in Medicine 27, 181–200 (2003)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Riccardo Bellazzi Ameen Abu-Hanna Jim Hunter

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Peelen, L., Klein, M.C.A., Schlobach, S., de Keizer, N.F., Peek, N. (2007). Analyzing Differences in Operational Disease Definitions Using Ontological Modeling. In: Bellazzi, R., Abu-Hanna, A., Hunter, J. (eds) Artificial Intelligence in Medicine. AIME 2007. Lecture Notes in Computer Science(), vol 4594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73599-1_40

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-73599-1_40

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73598-4

  • Online ISBN: 978-3-540-73599-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics