Xiang Y, Pant B, Eisen A, Beddoes M P, Poole D
Department of Computer Science, University of Regina, Sask., Canada.
Artif Intell Med. 1993 Aug;5(4):293-314. doi: 10.1016/0933-3657(93)90019-y.
A prototype neuromuscular diagnostic system (PAINULIM) that diagnoses painful or impaired upper limbs has been developed based on Bayesian networks. This paper presents nonmathematically the major knowledge representation issues that arose in the development of PAINULIM. Motivated by the computational overhead of large application domains, and the desire to provide a user with an interface that gives a focused display of a subdomain of current interest, we built PAINULIM using the idea of multiply sectioned Bayesian networks. A preliminary evaluation of PAINULIM with 76 patients has demonstrated good clinical performance.
一种基于贝叶斯网络开发的用于诊断上肢疼痛或功能受损的神经肌肉诊断系统原型（PAINULIM）已经问世。本文以非数学方式介绍了PAINULIM开发过程中出现的主要知识表示问题。受大型应用领域计算开销的影响，以及为用户提供一个能够集中展示当前感兴趣子领域的界面的愿望，我们利用多重分段贝叶斯网络的理念构建了PAINULIM。对76名患者进行的PAINULIM初步评估显示出良好的临床性能。
