Todd B S, Stamper R, Macpherson P
Programming Research Group, Oxford University Computing Laboratory, UK.
Int J Biomed Comput. 1993 Sep;33(2):129-48. doi: 10.1016/0020-7101(93)90030-a.
This paper explores a medical expert system combining techniques of Bayesian network modelling with ideas of weighted inference rules. The weights of the individual rules can be estimated objectively from a training set of actual cases; and they can be used in a Monte Carlo stimulation to estimate objectively conditional probabilities of diagnosis given particular combinations of symptoms. The paper describes and evaluates a medical expert system built according to this design. The diagnostic accuracy of the program was found to be similar to that obtained through the usual application of Bayes theorem with the assumption of conditional independence of symptoms given disease, even though the Bayesian classifier has more than 70 times as many numerical parameters. The method may be promising in cases where small training sets do not permit accurate estimation of large numbers of parameters.
本文探讨了一种将贝叶斯网络建模技术与加权推理规则相结合的医学专家系统。各个规则的权重可以从实际病例训练集中客观估计；并且可以在蒙特卡罗模拟中使用这些权重，以客观估计给定特定症状组合时的诊断条件概率。本文描述并评估了根据该设计构建的医学专家系统。发现该程序的诊断准确性与在症状给定疾病条件独立的假设下通过贝叶斯定理的常规应用所获得的诊断准确性相似，尽管贝叶斯分类器的数值参数数量是其70多倍。在小训练集不允许准确估计大量参数的情况下，该方法可能很有前景。
