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A causal-functional model applied to EMG diagnosis

  • Probabilistic Models and Fuzzy Logic
  • Conference paper
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Artificial Intelligence in Medicine (AIME 1997)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1211))

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Abstract

This paper presents an EMG diagnostic Knowledge Based System, that is the first application of our methodology for reasoning with causal-functional (meta-)models. Despite past difficulties, diagnosis is still an important application of KBSs, if considered in an appropriate context of medical practice. We argue that this is the case with neurophisiology, which lends to deep modelling of the domain and associated reasoning. The results obtained with our prototype system, and the clinical context where the system may be used make it a quite promising application, not only to experiment advanced artificial intelligence techniques but also to provide an useful decision support system for medical practice.

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References

  1. P. Barahona et al, Knowledge Processing for Decision Support in the Health Sector — A Perspective for the Next Decade, in Knowledge and Decisions in Health Telematics, P. Barahona and J.P.Christensen (Eds.), IOS Press, pp. 3–58, 1994.

    Google Scholar 

  2. J. Wyatt, Promoting Routine Use of Medical Knowledge Systems: the Lessons from Computerised ECG Interpreters, in Knowledge and Decisions in Health Telematics, P. Barahona and J.P.Christensen (Eds.), IOS Press, pp. 73–80, 1994.

    Google Scholar 

  3. I.D. Adams et al, Computer Aided Diagnosis of Acute Abdominal Pain: a Multicentre Study, British Medical Journal, no. 293, pp.800–804, 1986.

    Google Scholar 

  4. J.L Willems et al, The Diagnostic Performance of Computer Programs for the Interpretation of Electrocardiograms, New England Journal of Medicine, no. 325, pp. 1767–1773, 1991.

    Google Scholar 

  5. A. Fuglsang-Frederiksen, J. Rønagar and S. Vingtoft, PC-KANDID: An expert system for electromyography, Artificial Intelligence in Medicine, pp. 117–124, 1989.

    Google Scholar 

  6. A. Vila, D. Ziebelin and F. Reymond, Experimental EMG expert system as an aid in diagnosis, Electroenceph. Clin. Neurophysiol., no. 61, 1985.

    Google Scholar 

  7. S. Andreassen, B. Falck and K.G. Olesen, Diagnostic Function of the Microhuman Prototype of the Expert System MUNIN, Electroencephalography in Clinical Neurology, 1992.

    Google Scholar 

  8. M. Veloso et al, ESTEEM: European Standardised Telematics Tool to Evaluate EMG Knowledge Based Systems and Methods, in Health in the New Communication Age, M.F. Laires, M.J. Ladeira and J.P.Christensen (Eds.), IOS Press, pp. 348–356, 1995.

    Google Scholar 

  9. M. O'Neil, A. Glowinski and J. Fox, A Symbolic Theory of Decision Making Applied to Several Medical Tasks, in Lecture Notes in Medical Informatics, Springer-Verlag, no. 38, pp.62–71, 1989.

    Google Scholar 

  10. G. Lanzola and M. Stefanelli, A Specialized Framework for Medical Diagnostic Knowledge Based Systems, Computers and Biomedical Research, no. 25, pp. 351–365, 1992.

    Google Scholar 

  11. P. Barahona, A Causal — Functional Model for Medical Knowledge Representation, in Deep Models for Medical Knowledge Engineering, E. Keravnou (Ed.), Elsevier, pp. 101–127, 1992.

    Google Scholar 

  12. J. Cruz, A Causal-Functional Model for the Diagnosis of Neuromuscular Disorders (in Portuguese), M.Sc Thesis, Dep. of Computer Science, New University of Lisbon, 1995.

    Google Scholar 

  13. F. Menezes and P. Barahona, Defeasible Constraint Solving, Lecture Notes in Computer Science, Springer-Verlag, no. 1106, pp.151–170, 1996.

    Google Scholar 

  14. R. Beuscart, B. Modjeddi and M. DeMeester, ISAR: Integration System Architecture, in Health in the New Communication Age, M.F. Laires, M.J. Ladeira and J.P.Christensen (Eds.), IOS Press, pp. 392–403, 1995.

    Google Scholar 

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Authors and Affiliations

  1. Departamento de Informática, Universidade Nova Lisboa, 2825, Monte da Caparica, Portugal

    Jorge Cruz & Pedro Barahona

Authors
  1. Jorge Cruz
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  2. Pedro Barahona
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Editor information

Elpida Keravnou Catherine Garbay Robert Baud Jeremy Wyatt

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© 1997 Springer-Verlag Berlin Heidelberg

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Cruz, J., Barahona, P. (1997). A causal-functional model applied to EMG diagnosis. In: Keravnou, E., Garbay, C., Baud, R., Wyatt, J. (eds) Artificial Intelligence in Medicine. AIME 1997. Lecture Notes in Computer Science, vol 1211. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0029458

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  • DOI: https://doi.org/10.1007/BFb0029458

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-62709-8

  • Online ISBN: 978-3-540-68448-0

  • eBook Packages: Springer Book Archive

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