Li Weishi, Li Aiping, Li Shudong
Technol Health Care. 2015;23 Suppl 1:S55-9. doi: 10.3233/thc-150929.
Knowledge acquisition plays very important role in the diagnostic expert system. It usually takes a long period to acquire disease knowledge using the traditional methods. To solve this problem, this paper describes relations between rough set theory and rule-based description of diseases, which corresponds to the process of knowledge acquisition of diagnostic expert system. Then the exclusive rules, inclusive rules and disease images of disease are built based on the PDES diagnosis model, and the definition of probability rule is put forward. At last, the paper presents the rule-based automated induction reasoning method, including exhaustive search, post-processing procedure, estimation for statistic test and the bootstrap and resampling methods. We also introduce automated induction of the rule-based description, which is used in our diseases diagnostic expert system. The experimental results not only show that rough set theory gives a very suitable framework to represent processes of uncertain knowledge extraction, but also that this method induces diagnostic rules correctly. This method can act as the assistant tool for development of diagnosis expert system, and has an extensive application in intelligent information systems.
知识获取在诊断专家系统中起着非常重要的作用。使用传统方法获取疾病知识通常需要很长时间。为了解决这个问题,本文描述了粗糙集理论与基于规则的疾病描述之间的关系,这与诊断专家系统的知识获取过程相对应。然后基于PDES诊断模型构建了疾病的排他规则、包含规则和疾病图像,并提出了概率规则的定义。最后,本文提出了基于规则的自动归纳推理方法,包括穷举搜索、后处理过程、统计检验估计以及自助法和重采样方法。我们还介绍了基于规则描述的自动归纳,它被用于我们的疾病诊断专家系统。实验结果不仅表明粗糙集理论为表示不确定知识提取过程提供了一个非常合适的框架,而且该方法能正确地归纳诊断规则。该方法可作为诊断专家系统开发的辅助工具,在智能信息系统中具有广泛的应用。