Skip to main content
SpringerLink
Log in

A Model-Based System for Pacemaker Reprogramming

  • Conference paper
  • First Online:
Artificial Intelligence in Medicine (AIMDM 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1620))

Abstract

The process of reprogramming a cardiac pacemaker can be described in terms similar to those used for describing diagnostic problem solving. In this paper, the process of reprogramming a pacemaker is formalised as a special form of abductive diagnostic reasoning, where observable findings are interpreted with respect to results obtained from diagnostic tests. The dynamics of this process is cast as a diagnostic strategy, where information is gathered in a structured fashion. This abductive theory of pacemaker reprogramming has been used as the basis for a system that in its present form is capable of assisting cardiologists in dealing with problems in atrial sensing and pacing. The performance of the system has been evaluated using data from actual patients.

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

Access this chapter

Log in via an institution
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. L. Console, D. Theseider Dupré and P. Torasso, A theory of diagnosis for incomplete causal models, in: Proceedings of the 10th International Joint Conference on Artificial Intelligence, Los Angeles, USA (1989) 1311–1317.

    Google Scholar 

  2. K.J. Isselbacher, et al., Harrison’s Principles of Internal Medicine, 13th edition, McGraw-Hill, New-York, 1994.

    Google Scholar 

  3. P.J.F. Lucas, Symbolic diagnosis and its formalisation, The Knowledge Engineering Review 12(2) (1997) 109–146.

    Article  MathSciNet  Google Scholar 

  4. P.J.F. Lucas, Analysis of notions of diagnosis, Artificial Intelligence 105 (1998) 295–343.

    Article  MATH  MathSciNet  Google Scholar 

  5. D. Poole, Local Users Guide to Theorist, Report, Department of Computer Science, University of British Columbia, Vancouver, 1990.

    Google Scholar 

  6. D. Poole, Probabilistic Horn abduction and Bayesian networks, Artificial Intelligence 64 (1993) 81–129.

    Article  MATH  Google Scholar 

  7. R. Reiter, On closed world data bases, in: H. Gallaire and J. Minker, eds., Logic and Databases (Plenum-Press, New York, 1978) 55–76.

    Google Scholar 

  8. R. Reiter, A logic for default reasoning, Artificial Intelligence 13 (1980) 81–132.

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Utrecht University, P.O. Box 80089, 3508, TB Utrecht, The Netherlands

    Peter Lucas

  2. Research and Development Department, Vitatron Medical B.V., P.O. Box 76, 6950, AB Dieren, The Netherlands

    Astrid Tholen & Geeske van Oort

Authors
  1. Peter Lucas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Astrid Tholen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Geeske van Oort
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Austrian Research Institute for Artificial Intelligence, Schottengasse 3, A-1010, Vienna, Austria

    Werner Horn

  2. Department of Medicine and Computer Science, Stanford University, 251 Campus Drive, Stanford, CA, 94305-5479, USA

    Yuval Shahar

  3. Department of Medicine Karolinska Institutet, Huddinge University Hospital, Sweden

    Greger Lindberg

  4. Department of Medical Informatics and Image Analysis, Aalborg University, Fredrik Bajers Vej 7D, DK-9000, Aalborg, Denmark

    Steen Andreassen

  5. School of Public Policy, University College London, 29/30 Tavistock Square, London, WC1H 9EZ, UK

    Jeremy Wyatt

Rights and permissions

Reprints and permissions

Copyright information

© 1999 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Lucas, P., Tholen, A., van Oort, G. (1999). A Model-Based System for Pacemaker Reprogramming. In: Horn, W., Shahar, Y., Lindberg, G., Andreassen, S., Wyatt, J. (eds) Artificial Intelligence in Medicine. AIMDM 1999. Lecture Notes in Computer Science(), vol 1620. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48720-4_19

Download citation

  • DOI: https://doi.org/10.1007/3-540-48720-4_19

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66162-7

  • Online ISBN: 978-3-540-48720-3

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation