面向医疗诊断的BN-CBR混合模型及其应用
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摘要
目前CBR医疗辅助诊断系统相似度准则不能随案例增加进行更新,无法实现动态学习,模型检索效率随案例库规模增加而降低,在医疗数据缺失条件下诊断准确性较低。针对这些问题,建立贝叶斯网络-基于案例推理(BN-CBR)混合诊断模型,解决医疗诊断数据缺失问题,满足案例库更新要求,提高诊断效率与正确性。
论文将贝叶斯网络(BN)与基于案例推理方法(CBR)结合建立BN-CBR混合诊断模型。模型以CBR作为基本的推理机制,通过检索以往案例为当前诊断问题提供决策支持。BN作为一种数据挖掘工具对案例库进行学习获得诊断属性间存在的潜在关联知识,将这些知识通过相似度评价函数反映到模型的案例检索过程中。在此基础上,提出通过K-D树算法组织案例库,按照诊断属性权重将案例库划分为不同的案例子集,模型在各案例子集中进行检索,这样大大压缩了检索空间,提高案例检索效率。利用信息增益算法从大量诊断属性中筛选最优属性子集,利用自省学习方法为每个属性分配权重。提出了通过人机结合方式进行案例改写和重用的思路,由医生对案例进行改写,将改写操作过程作为案例建立改写案例库,由BN-CBR混合模型进行改写案例检索,为医生改写案例提供决策支持。
定义了基于CBR的医疗诊断概念模型,建立了BN-CBR混合诊断模型。混合模型通过相似匹配函数的更新实现推理的动态性,通过K-D树算法组织诊断案例建立案例库。通过心脏病诊断实例分析,BN-CBR混合诊断模型解决了医疗数据缺失问题,有效提高了检索的效率和诊断准确率,实现了模型动态学习。
At present, the similarity criteria of CBR medical diagnosis systems can not be updated with the increase in case, can not be achieved dynamic study. The efficiency of CBR model retrieval is reducing owing to size of case base increase. Particularly, because of the loss of medical data the diagnostic accuracy is unsatisfactory. To solve these problems by establishment of BN-CBR hybrid diagnosis model, look for solutions of missing data problem, meet the requirements of case base update and improve the efficiency and accuracy of model diagnosis.
The thesis employ Bayesian Networks (BN) and Case-based Reasoning methods (CBR) to establish BN-CBR hybrid diagnosis model. The Model is based on CBR as the basic reasoning mechanism, by searching the past case for the current diagnosis problem. BN as a data mining tool to study the knowledge of the potential relevance between the medicine properties about Patient, use this knowledge in the process of case retrieval through the Similarity Evaluation function. Furthermore this article organized case base through the K-D Tree algorithm. The algorithm divide case base into of different cases according to attribute weight, so that model retrieval can focus on the case subsets, this greatly reduced the search space and improve the efficiency of case retrieval. Using Information Gain algorithm select the optimal attribute subset from a large number of diagnostic properties. Using the Self-learning method distribute weight to each diagnostic attribute. We proposed through a combination of man-machine method to revise & reuse the cases, carried out by physician revise case, record the process and establish revise case base simultaneously by the BN-CBR hybrid model, to provide the decision-making support for physician revise cases.
Proposed the conceptual model definition of CBR based medical diagnosis, established the BN-CBR hybrid medical diagnostic model. Similarity function of the model can be dynamic reasoning through automatic updates. We organized new case base by the K-D Tree algorithm. Through heart disease diagnosis case study, BN-CBR hybrid diagnosis model can be solve the problem of missing medical data, to effectively improve the retrieval efficiency and accuracy of diagnosis, and realization of dynamic learning.
论文将贝叶斯网络(BN)与基于案例推理方法(CBR)结合建立BN-CBR混合诊断模型。模型以CBR作为基本的推理机制,通过检索以往案例为当前诊断问题提供决策支持。BN作为一种数据挖掘工具对案例库进行学习获得诊断属性间存在的潜在关联知识,将这些知识通过相似度评价函数反映到模型的案例检索过程中。在此基础上,提出通过K-D树算法组织案例库,按照诊断属性权重将案例库划分为不同的案例子集,模型在各案例子集中进行检索,这样大大压缩了检索空间,提高案例检索效率。利用信息增益算法从大量诊断属性中筛选最优属性子集,利用自省学习方法为每个属性分配权重。提出了通过人机结合方式进行案例改写和重用的思路,由医生对案例进行改写,将改写操作过程作为案例建立改写案例库,由BN-CBR混合模型进行改写案例检索,为医生改写案例提供决策支持。
定义了基于CBR的医疗诊断概念模型,建立了BN-CBR混合诊断模型。混合模型通过相似匹配函数的更新实现推理的动态性,通过K-D树算法组织诊断案例建立案例库。通过心脏病诊断实例分析,BN-CBR混合诊断模型解决了医疗数据缺失问题,有效提高了检索的效率和诊断准确率,实现了模型动态学习。
At present, the similarity criteria of CBR medical diagnosis systems can not be updated with the increase in case, can not be achieved dynamic study. The efficiency of CBR model retrieval is reducing owing to size of case base increase. Particularly, because of the loss of medical data the diagnostic accuracy is unsatisfactory. To solve these problems by establishment of BN-CBR hybrid diagnosis model, look for solutions of missing data problem, meet the requirements of case base update and improve the efficiency and accuracy of model diagnosis.
The thesis employ Bayesian Networks (BN) and Case-based Reasoning methods (CBR) to establish BN-CBR hybrid diagnosis model. The Model is based on CBR as the basic reasoning mechanism, by searching the past case for the current diagnosis problem. BN as a data mining tool to study the knowledge of the potential relevance between the medicine properties about Patient, use this knowledge in the process of case retrieval through the Similarity Evaluation function. Furthermore this article organized case base through the K-D Tree algorithm. The algorithm divide case base into of different cases according to attribute weight, so that model retrieval can focus on the case subsets, this greatly reduced the search space and improve the efficiency of case retrieval. Using Information Gain algorithm select the optimal attribute subset from a large number of diagnostic properties. Using the Self-learning method distribute weight to each diagnostic attribute. We proposed through a combination of man-machine method to revise & reuse the cases, carried out by physician revise case, record the process and establish revise case base simultaneously by the BN-CBR hybrid model, to provide the decision-making support for physician revise cases.
Proposed the conceptual model definition of CBR based medical diagnosis, established the BN-CBR hybrid medical diagnostic model. Similarity function of the model can be dynamic reasoning through automatic updates. We organized new case base by the K-D Tree algorithm. Through heart disease diagnosis case study, BN-CBR hybrid diagnosis model can be solve the problem of missing medical data, to effectively improve the retrieval efficiency and accuracy of diagnosis, and realization of dynamic learning.
引文
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