Li Aiping, Jin Songchang, Zhang Lumin, Jia Yan
Technol Health Care. 2015;23 Suppl 1:S37-42. doi: 10.3233/thc-150926.
Although diagnostic expert systems using a knowledge base which models decision-making of traditional experts can provide important information to non-experts, they tend to duplicate the errors made by experts. Decision-Theoretic Model (DTM) is therefore very useful in expert system since they prevent experts from incorrect reasoning under uncertainty. For the diagnostic expert system, corresponding DTM and arithmetic are studied and a sequential diagnostic decision-theoretic model based on Bayesian Network is given. In the model, the alternative features are categorized into two classes (including diseases features and test features), then an arithmetic for prior of test is provided. The different features affect other features weights are also discussed. Bayesian Network is adopted to solve uncertainty presentation and propagation. The model can help knowledge engineers model the knowledge involved in sequential diagnosis and decide evidence alternative priority. A practical example of the models is also presented: at any time of the diagnostic process the expert is provided with a dynamically updated list of suggested tests in order to support him in the decision-making problem about which test to execute next. The results show it is better than the traditional diagnostic model which is based on experience.
尽管使用基于传统专家决策制定模型的知识库的诊断专家系统可以为非专家提供重要信息，但它们往往会重复专家所犯的错误。因此，决策理论模型（DTM）在专家系统中非常有用，因为它们可以防止专家在不确定性情况下进行错误推理。针对诊断专家系统，研究了相应的DTM和算法，并给出了一种基于贝叶斯网络的顺序诊断决策理论模型。在该模型中，将备选特征分为两类（包括疾病特征和检验特征），然后给出了一种检验先验的算法。还讨论了不同特征对其他特征权重的影响。采用贝叶斯网络解决不确定性表示和传播问题。该模型可以帮助知识工程师对顺序诊断中涉及的知识进行建模，并确定证据备选优先级。还给出了该模型的一个实际例子：在诊断过程的任何时候，都会为专家提供一个动态更新的建议检验列表，以支持他解决关于下一步执行哪个检验的决策问题。结果表明，它比基于经验的传统诊断模型更好。
