DownloadThe Portobello Bookshop Gift Guide 2024

AIME 87

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

John Fox editor Marius Fieschi editor Rolf Engelbrecht editor

Format:Paperback

Publisher:Springer-Verlag Berlin and Heidelberg GmbH & Co. KG

Published:24th Aug '87

Should be back in stock very soon

AIME 87 cover

Springer Book Archives

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.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.

ISBN: 9783540184027

Dimensions: unknown

Weight: unknown

255 pages