AIME 87

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

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.

• European Conference on Artificial Intelligence
• Knowledge Engineering
• Prolog
• Radiologieinformationssystem
• artificial intelligence
• classification
• diagnosis
• expert system
• intelligence
• knowledge
• knowledge representation
• learning
• machine learning
• simulation
• uncertainty

• 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

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