Jamieson P W
University of Kansas Medical Center, Kansas City.
J Clin Eng. 1990 Sep-Oct;15(5):371-80. doi: 10.1097/00004669-199009000-00012.
Medical expert systems frequently use causal models to capture knowledge and diagnostic-problem-solving expertise. A significant obstacle confronting these systems is providing informative explanations without prohibitive computational expense. The explanations should allow the user to understand the decisions of the expert system and obtain additional details when needed. A new method, called HyperExplain, has been devised to flexibly link explanations with conclusions generated by a causal reasoning system. This approach creates a patient specific explanatory (PSE) model for the medical expert system that provides decision support from a variety of perspectives. A key feature of this method is the ability to alter the focus of explanations depending upon the problem-solving context and patient manifestations. The method has been implemented in a program that provides diagnostic assistance to physicians in the domain of neurophysiology.
医学专家系统经常使用因果模型来获取知识和诊断问题解决的专业知识。这些系统面临的一个重大障碍是在不产生过高计算成本的情况下提供信息丰富的解释。这些解释应使用户能够理解专家系统的决策，并在需要时获取更多细节。一种名为HyperExplain的新方法已经被设计出来，用于将解释与因果推理系统生成的结论灵活地联系起来。这种方法为医学专家系统创建了一个患者特定的解释（PSE）模型，该模型从各种角度提供决策支持。该方法的一个关键特性是能够根据问题解决的背景和患者表现改变解释的重点。该方法已在一个为神经生理学领域的医生提供诊断辅助的程序中实现。
