AIME 87 : European Conference on Artificial Intelligence in Medicine, Marseilles, August 31st-September 3rd 1987 : proceedings


AIME 87 : European Conference on Artificial Intelligence in Medicine, Marseilles, August 31st-September 3rd 1987 : proceedings

J. Fox, M. Fieschi, R. Engelbrecht (eds.)

(Lecture notes in medical informatics, 33)

Springer-Verlag, c1987

  • : gw
  • : us

大学図書館所蔵 件 / 4



Organized by the European Society for Artificial Intelligence in Medicine and others

Includes bibliographies



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.


Methodology.- "Intermed": A Medical Language Interface.- Inference Engineering through Prototyping in Prolog.- The Evaluation of Clinical Decision Support Systems: A Discussion of the Methodology used in the ACORN Project.- Matching Patients: An Approach for Decision Support in Liver Transplantation.- Clinical Applications (1).- An Expert System for Diagnosis and Therapy Planning in Patients with Peripheral Vascular Disease.- An Expert System for the Classification of Dizziness and Vertigo.- The Senex System: A Microcomputer-Based Expert System Built by Oncologists for Breast Cancer Management.- Qualitative Reasoning.- The Use of QSIM for Qualitative Simulation of Physiological Systems.- Qualitative Description of Electrophysiologic Measurements: towards automatic data interpretation.- A Qualitative Spatial Representation for Cardiac Electrophysiology.- Knowledge Acquisition and Representation.- Knowledge Acquisition in Expert System Assisted Diagnosis: A Machine Learning Approach.- Knowledge Representation for Cooperative Medical Systems.- A Representation of Time for Medical Expert Systems.- Management of Uncertainty.- TOULMED, an Inference Engine Which Deals With Imprecise and Uncertain Aspects of Medical Knowledge.- Coherent Handling of Uncertainty Via Localized Computation in an Expert System for Therapeutic Decision.- MUNIM - On the Case for Probabilities in Medical Expert Systems - a Practical Exercise.- Rule Based Expert Systems in Gynecology: Statistical Versus Heuristic Approach.- Knowledge Engineering Tools.- A Radiological Expert System for the PC - Design and Implementation Issues.- A PC-Based Shell for Clinical Information Systems With Reasoning Capabilities.- The Kernel Mechanism for Handling Assumptions and Justifications and its Application to the Biotechnologies.- General Session.- Man-Machine Interaction in Check.- The Oxford System of Medicine: A Prototype Information System for Primary Care.- Clinical Applications (2).- Evaluating the Performance of Anemia.- Computer Aided Diagnosis and Treatment of Brachial Plexus Injuries.- Representation of Embryonic Development and its Anomalies.- A Micro Computer Based Decision Support for Lipid Disorder.

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