Intelligent platform for automatic medical knowledge acquisition: detection and understanding of neural dysfunctions

• Published in: 16th IEEE Symposium Computer-Based Medical Systems, 2003. Proceedings pp. 136 - 141
• Main Authors: Zorman, M., Kokol, P., Lenic, M., Povalej, P., Stiglic, B., Flisar, D.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2003
• Subjects: Data mining; Decision making; Electroencephalography; Intelligent systems; Knowledge acquisition; Knowledge representation; Learning systems; Machine learning; Nervous system; Testing
• ISBN: 0769519016; 9780769519012
• ISSN: 1063-7125
• DOI: 10.1109/CBMS.2003.1212779

The use of intelligent systems and machine learning methods, capable of automatic decision making based on already solved cases, and data mining, are getting more and more popular. Here we are faced not only with technical problems, but also with limited confidence in machine learning techniques. In some cases methods that may explicitly show the deduction process are not powerful enough. One of the possibilities is to modify/improve the methods so that the users could easily follow the process of decision making. To solve this problem, a few years ago we started to develop a platform, which enables us to develop, test and use different kinds of hybrid methods. These are meant to combine the advantages of the integrated methods-e.g., power and knowledge representation-that contribute to the quality of the acquired knowledge. In this paper we present a way of using the developed platform in order to obtain new knowledge, based on results from neurophysiological measurements We are every pleased with the performance of our intelligent platform. The first results we obtained already show some improvement in comparison to classic machine learning approaches.