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AIME 87

European Conference on Artificial Intelligence in Medicine Marseilles, August 31st – September 3rd 1987 Proceedings

  • Conference proceedings
  • © 1987

Overview

Editors:
  1. John Fox

    1. Biomedical Computing Unit, Imperial Cancer Research Fund Laboratories, London, UK

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  2. Marius Fieschi

    1. Laboratoire d’Informatique Médical de la Faculté de Médecine, Université de Marseille, Marseille Cédex 5, France

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  3. Rolf Engelbrecht

    1. MEDIS-Institut, Gesellschaft für Strahlen- und Umweltforschung mbH München, Neuherberg, Federal Republic of Germany

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Part of the book series: Lecture Notes in Medical Informatics (LNMED, volume 33)

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Table of contents (26 papers)

  1. Front Matter

    Pages N2-X
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  2. Methodology

    1. Front Matter

      Pages 1-1
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    2. “INTERMED”: A Medical Language Interface

      • Christian Mery, Bernard Normier, Antoine Ogonowski
      Pages 3-8

    3. Inference Engineering through Prototyping in Prolog

      • Josef van Thillo, Adolf Mulders
      Pages 9-14

    4. The evaluation of clinical decision support systems: a discussion of the methodology used in the ACORN project

      • Jeremy Wyatt
      Pages 15-24

    5. Matching Patients: An Approach for Decision Support in Liver Transplantation

      • Günter M. Tusch, Jochen Bernauer, P. L. Reichertz
      Pages 25-33

  3. Clinical Applications (1)

    1. Front Matter

      Pages 35-35
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    2. An Expert System for Diagnosis and Therapy Planning in Patients with Peripheral Vascular Disease

      • Jan L. Talmon, Ruud A. J. Schijven, Peter J. E. H. M. Kitslaar, Renée Penders
      Pages 37-44

    3. An Expert System for the Classification of Dizziness and Vertigo

      • R. Schmid, P. Zanocco, A. Buizza, G. Magenes, M. Manfrin, E. Mira
      Pages 45-53

    4. The SENEX System : A Microcomputer-Based Expert System Built by Oncologists for Breast Cancer Management

      • Jean-Louis Renaud-Salis, Françoise Bonichon, Michel Durand, Antoine Avril, Claude Lagarde, J. P. Serre et al.
      Pages 54-70

  4. Qualitative Reasoning

    1. Front Matter

      Pages 71-71
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    2. The Use of QSIM for Qualitative Simulation of Physiological Systems

      • Emma Nicolosi, Mark S. Leaning
      Pages 73-80

    3. Qualitative Description of Electrophysiologic Measurements: towards automatic data interpretation

      • W. J. Irler, R. Antolini, M. Kirchner, L. Stringa
      Pages 81-87

    4. A Qualitative Spatial Representation for Cardiac Electrophysiology

      • Nick Gotts
      Pages 88-95

  5. Knowledge Acquisition and Representation

    1. Front Matter

      Pages 97-97
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    2. Knowledge acquisition in expert system assisted diagnosis : a machine learning approach

      • M. Funk, R. D. Appel, Ch. Roch, D. Hochstrasser, Ch. Pellegrini, A. F. Müller
      Pages 99-103

    3. Knowledge Representation for Cooperative Medical Systems

      • A. L. Rector
      Pages 104-111

    4. A Representation of Time for Medical Expert Systems

      • Ian Hamlet, Jim Hunter
      Pages 112-119

  6. Management of Uncertainty

    1. Front Matter

      Pages 121-121
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    2. TOULMED, an inference engine which deals with imprecise and uncertain aspects of medical knowledge

      • J. C. Buisson, H. Farreny, H. Prade, M. C. Turnin, J. P. Tauber, F. Bayard
      Pages 123-140
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Keywords

About this book

The current scarcity of expert systems where the reasoning is based on Bayesian probability theory may be due to misconceptions about probabilities found in the literature. As argued by Cheeseman (1985), these misconceptions have led to the attitude: "The Bayesian approach doesn't work - so here is a new scheme". Several of these expert systems based on ad hoc "probability" concepts have been successful in a number of ways, demonstrating the necessity of being able to handle uncertainty in medical expert systems. They also demonstrate the need for a theoretically sound handling of uncertainty. In Andersen et al. (1986) it was postulated that knowledge organized in a causal network can be used for a unified approach to the main tasks of a medical expert system: diagnosis, planning of tests and explanations. The present paper explores this postulate in a causal probabilistic network. It also provides a practical demonstration that the problems supposedly associated with probabilistic networks are either non-existent or that practical solutions can be found. This paper reports on the methods implemented in MUNIN* -an expert system for electromyography (EMG) (Andreassen et al. 1987). EMG is the diagnosis of muscle and nerve diseases through analysis of bioelectrical signals from muscle and nerve tissue. In Andreassen et al.

Editors and Affiliations

  • Biomedical Computing Unit, Imperial Cancer Research Fund Laboratories, London, UK

    John Fox

  • Laboratoire d’Informatique Médical de la Faculté de Médecine, Université de Marseille, Marseille Cédex 5, France

    Marius Fieschi

  • MEDIS-Institut, Gesellschaft für Strahlen- und Umweltforschung mbH München, Neuherberg, Federal Republic of Germany

    Rolf Engelbrecht

Bibliographic Information

  • Book Title: AIME 87

  • Book Subtitle: European Conference on Artificial Intelligence in Medicine Marseilles, August 31st – September 3rd 1987 Proceedings

  • Editors: John Fox, Marius Fieschi, Rolf Engelbrecht

  • Series Title: Lecture Notes in Medical Informatics

  • DOI: https://doi.org/10.1007/978-3-642-95549-5

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer-Verlag Berlin Heidelberg 1987

  • Softcover ISBN: 978-3-540-18402-7Published: 24 August 1987

  • eBook ISBN: 978-3-642-95549-5Published: 06 December 2012

  • Series ISSN: 0172-7788

  • Edition Number: 1

  • Number of Pages: X, 255

  • Topics: Health Informatics, Computer Appl. in Life Sciences

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