Method of Refining Knowledge in Oriental Medicine by Sample Cases

概要

論文の詳細を見る
In general, the work on developing an expert system has relied on domain experts to provide all domain-specific knowledge. The method for acquiring knowledge directly from experts is inadequate in oriental medicine because it is hard to find an appropriate expert and the development cost becomes too high. Therefore, we have developed two effective methods for acquiring knowledge indirectly from sample cases. One is to refine a constructed knowledge base by using sample cases. The other is to train a neural network by using sample cases. To demonstrate the effectiveness of our methods, we have implemented two prototype systems; the Oriental Medicine Expert System (OMES) and the Oriental Medicine Neural Network (OMNN). These systems have been compared with the system with the knowledge base built directly by domain experts (OLDS). Among these systems, OMES are considered to be superior to other systems in terms of performances, development costs, and practicalness. In this paper, we present our methods, and describe our experimental and comparison results.
社団法人電子情報通信学会の論文
1993-02-25