Hybrid expert system for decision supporting in the medical area: complexity and cognitive computing.

This paper proposes a hybrid expert system (HES) to minimise some complexity problems pervasive to the artificial intelligence such as: the knowledge elicitation process, known as the bottleneck of expert systems; the model choice for knowledge representation to code human reasoning; the number of neurons in the hidden layer and the topology used in the connectionist approach; the difficulty to obtain the explanation on how the network arrived to a conclusion. Two algorithms applied to developing of HES are also suggested. One of them is used to train the fuzzy neural network and the other to obtain explanations on how the fuzzy neural network attained a conclusion. To overcome these difficulties the cognitive computing was integrated to the developed system. A case study is presented (e.g. epileptic crisis) with the problem definition and simulations. Results are also discussed.

A hybrid expert system for the diagnosis of epileptic crisis.

This work presents a hybrid expert system (HES) intended to minimise some complex problems pervasive to knowledge engineering such as: the knowledge elicitation process, known as the bottleneck of expert systems; the choice of a model for knowledge representation to codify human reasoning; the number of neurons in the hidden layer and the topology used in the connectionist approach; the difficulty to extract an explanation from the network. Two algorithms applied to developing of HES are also suggested. One of them is used to train the fuzzy neural network and the other to obtain explanations on how the fuzzy neural network attained a conclusion. A case study is presented (e.g. epileptic crisis) with the inclusion of problem definition and simulations. The results are also discussed.

Connectionist expert systems as medical decision aid.

In this paper neural networks are used as associative memories to build an expert system for aiding medical diagnosis. As in expert systems using symbolic manipulation, the knowledge is introduced by a knowledge engineer using a collection of known cases. The system has an object-oriented approach to knowledge organization and the resulting network topology. Fuzzy sets are used to interpret connection values and/or excitation state of the units. The main result is that the proposed neural network allows not only finding a solution in some cases, but also suggests obtaining more clinical data if the data available is insufficient to reach a conclusion. This approach is illustrated by examples.