Haddawy P, Jacobson J, Kahn C E
Department of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee.
Proc Annu Symp Comput Appl Med Care. 1994:770-4.
We present a system that generates explanations and tutorial problems from the probabilistic information contained in Bayesian belief networks. BANTER is a tool for high-level interaction with any Bayesian network whose nodes can be classified as hypotheses, observations, and diagnostic procedures. Users need no knowledge of Bayesian networks, only familiarity with the particular domain and an elementary understanding of probability. Users can query the knowledge base, identify optimal diagnostic procedures, and request explanations. We describe BANTER's algorithms and illustrate its application to an existing medical model.
我们提出了一种系统，该系统可根据贝叶斯信念网络中包含的概率信息生成解释和教程问题。BANTER是一种用于与任何贝叶斯网络进行高级交互的工具，其节点可分为假设、观察结果和诊断程序。用户无需了解贝叶斯网络，只需熟悉特定领域并对概率有基本的理解即可。用户可以查询知识库、确定最佳诊断程序并请求解释。我们描述了BANTER的算法，并说明了其在现有医学模型中的应用。
