MUNIN: a causal probabilistic network for interpretation of electromyographic findings

Abstract

Experience gained through building a causal network for interpretation of electromyographic findings has shown that probabilistic inference is a realistic possibility in networks of non-trivial size. The use of nodes with many internal states has made it possible to make a conceptually simple and compact representation of knowledge. Deep in the form of pathophysiological models are used to reduce the problem of estimating thousands of conditional probabilities to a manageble size. The network has built-in mechanisms that will detect when the network is confronted with a situation outside the limits of its own knowledge and it handles conflicting evidence in a simple and consistent way.