基于贝叶斯网络的轻度认知障碍诊断系统  

Diagnostic System of Mild Cognitive Impairment Based on Bayesian Network

作　　者：孙岩[1,2] 唐一源[3] 

机构地区：[1]辽宁师范大学心理发展与教育研究中心,辽宁大连116029 [2]辽宁师范大学教育学院心理系,辽宁大连116029 [3]大连理工大学神经信息学研究所,辽宁大连116024

出　　处：《电子科技大学学报》2012年第3期336-341,共6页Journal of University of Electronic Science and Technology of China

基　　金：国家自然科学基金(60971096);国家社会科学基金重点项目(11AZD089)

摘　　要：提出一种基于依赖性分析和打分函数进行贝叶斯网络结构学习的新方法,并把该方法应用于轻度认知障碍诊断系统中。该算法首先通过对结点变量间的依赖性分析测试和无向图的遍历,获得贝叶斯网络结构中所有结点的先验顺序,然后用启发式打分—搜索方法获得最优的贝叶斯网络结构。实验结果表明,该算法能够在不增加算法复杂度的情况下,完成无结点顺序要求的贝叶斯网络学习,并能应用于轻度认知障碍诊断系统中,实现较好的预测,进而辅助医生的诊断。This paper presents a new method in structure learning of Bayesian network based on dependency analysis and scoring function. Through analyzing the dependent relationship between variables and accessing to undirected graph, the prior sequence of all of the nodes in Bayesian network structure is obtained. The optimal structure of the Bayesian network is then generated by heuristic-search method. The new algorithm has been applied to the diagnostic system of mild cognitive impairment. The experimental results show that the new algorithm can better predict the possibility of mild cognitive impairment under the similar complexity, and further assist the diagnosis of doctor.

关 键 词：贝叶斯网络 诊断系统 依赖性分析 轻度认知障碍 

分 类 号：TP182[自动化与计算机技术—控制理论与控制工程]