Tsumoto S, Tanaka H
Department of Informational Medicine, Medical Research Institute, Tokyo Medical and Dental University, Japan.
Proc Annu Symp Comput Appl Med Care. 1994:1066-70.
Automated knowledge acquisition is an important research issue in improving the efficiency of medical expert systems. Rules for medical expert systems consists of two parts: one is a proposition part, which represent a if-then rule, and the other is probabilistic measures, which represents reliability of that rule. Therefore, acquisition of both knowledge is very important for application of machine learning methods to medical domains. Extending concepts of rough set theory to probabilistic domain, we introduce a new approach to knowledge acquisition, which induces probabilistic rules based on rough set theory (PRIMEROSE) and develop a program that extracts rules for an expert system from clinical database, using this method. The results show that the derived rules almost correspond to those of medical experts.
自动知识获取是提高医学专家系统效率的一个重要研究问题。医学专家系统的规则由两部分组成：一部分是命题部分，它表示一个“如果……那么……”规则；另一部分是概率度量，它表示该规则的可靠性。因此，获取这两方面的知识对于将机器学习方法应用于医学领域非常重要。将粗糙集理论的概念扩展到概率领域，我们引入了一种新的知识获取方法，即基于粗糙集理论诱导概率规则（PRIMEROSE），并开发了一个程序，使用这种方法从临床数据库中为专家系统提取规则。结果表明，导出的规则几乎与医学专家的规则一致。
