Todd B S
Oxford University Computing Laboratory, UK.
Med Inform (Lond). 1994 Jul-Sep;19(3):209-27. doi: 10.3109/14639239409025328.
I present a formal, mathematical specification of a probabilistic expert system to assist the localization of nerve lesions. The program is based on an anatomical model of the peripheral nervous system of the human upper limb. The simulation model defines a joint probability distribution over the states of nerves and clinical manifestations. A simple, general-purpose heuristic algorithm is used to approximate conditional probabilities of interest. It is shown how an upper bound on the expected approximation error can be measured experimentally; this upper bound is 0.05 for the system described here, although the bound can be made arbitrarily small by expending more computational effort. The expert system is compared with the nearest-neighbour statistical classification rule on two databases of 26 and 25 cases respectively. The expert system makes fewer errors, although the observed difference does not reach statistical significance. Possible future refinements to the model are explored, and the advantages of specifying expert systems formally are discussed.
我提出了一个用于辅助神经损伤定位的概率专家系统的形式化数学规范。该程序基于人类上肢周围神经系统的解剖模型。模拟模型定义了神经状态和临床表现的联合概率分布。使用一种简单的通用启发式算法来近似感兴趣的条件概率。展示了如何通过实验测量预期近似误差的上限；对于此处描述的系统，该上限为0.05，尽管通过花费更多计算量可以使该上限任意小。分别在两个包含26个和25个病例的数据库上，将该专家系统与最近邻统计分类规则进行了比较。该专家系统产生的错误较少，尽管观察到的差异未达到统计学显著性。探讨了该模型未来可能的改进，并讨论了形式化指定专家系统的优点。
