Boegl Karl, Adlassnig Klaus-Peter, Hayashi Yoichi, Rothenfluh Thomas E, Leitich Harald
Section on Medical Expert and Knowledge-Based Systems, Department of Medical Computer Sciences, University of Vienna, Vienna, Austria.
Artif Intell Med. 2004 Jan;30(1):1-26. doi: 10.1016/s0933-3657(02)00073-8.
This paper describes the fuzzy knowledge representation framework of the medical computer consultation system MedFrame/CADIAG-IV as well as the specific knowledge acquisition techniques that have been developed to support the definition of knowledge concepts and inference rules. As in its predecessor system CADIAG-II, fuzzy medical knowledge bases are used to model the uncertainty and the vagueness of medical concepts and fuzzy logic reasoning mechanisms provide the basic inference processes. The elicitation and acquisition of medical knowledge from domain experts has often been described as the most difficult and time-consuming task in knowledge-based system development in medicine. It comes as no surprise that this is even more so when unfamiliar representations like fuzzy membership functions are to be acquired. From previous projects we have learned that a user-centered approach is mandatory in complex and ill-defined knowledge domains such as internal medicine. This paper describes the knowledge acquisition framework that has been developed in order to make easier and more accessible the three main tasks of: (a) defining medical concepts; (b) providing appropriate interpretations for patient data; and (c) constructing inferential knowledge in a fuzzy knowledge representation framework. Special emphasis is laid on the motivations for some system design and data modeling decisions. The theoretical framework has been implemented in a software package, the Knowledge Base Builder Toolkit. The conception and the design of this system reflect the need for a user-centered, intuitive, and easy-to-handle tool. First results gained from pilot studies have shown that our approach can be successfully implemented in the context of a complex fuzzy theoretical framework. As a result, this critical aspect of knowledge-based system development can be accomplished more easily.
本文描述了医学计算机咨询系统MedFrame/CADIAG-IV的模糊知识表示框架，以及为支持知识概念和推理规则的定义而开发的特定知识获取技术。与其前身系统CADIAG-II一样，模糊医学知识库用于对医学概念的不确定性和模糊性进行建模，模糊逻辑推理机制提供基本的推理过程。从医学领域专家那里引出和获取医学知识，通常被认为是医学领域基于知识的系统开发中最困难、最耗时的任务。毫不奇怪，当要获取像模糊隶属函数这样不熟悉的表示时，情况更是如此。从以前的项目中我们了解到，在诸如内科等复杂且定义不明确的知识领域，以用户为中心的方法是必不可少的。本文描述了为使以下三项主要任务更轻松、更易于实现而开发的知识获取框架：(a)定义医学概念；(b)为患者数据提供适当的解释；(c)在模糊知识表示框架中构建推理知识。特别强调了一些系统设计和数据建模决策的动机。理论框架已在一个软件包——知识库构建工具包中实现。该系统的概念和设计反映了对以用户为中心、直观且易于操作的工具的需求。初步研究取得的结果表明，我们的方法可以在复杂的模糊理论框架背景下成功实施。因此，可以更轻松地完成基于知识的系统开发的这一关键方面。
