基于案例库的诊疗决策支持技术研究
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摘要
医院诊疗决策过程的知识需求具有多属性、完整性、原始性、即时性、易接受性和动态更新等典型特征，而这正是基于案例推理（CBR）进行信息处理的技术优势所在。但由于人类所患疾病的多样性、多变性、复杂性、不确定性和动态演化性等特点，以及医学技术和诊断方法自身的发展变化，加之医院各种用于早期探测、诊断、预测等诊疗决策过程的历史病例中缺失信息、判别变量缺失、区间数、灰值等情况的存在，现有的案例推理技术无法满足医院诊疗决策问题解决的需要，尚存在着一些具体问题亟待进一步的研究，如：①复杂多属性诊疗决策案例的属性选择问题，多属性诊疗决策案例尤其是不含判别变量案例的权重获取方法问题；②含区间模糊数、空间方位离散数值、连续数值等混合数值诊疗案例的模糊多属性决策问题；③最近邻法在连续型属性的计算上具有较高的效率但在离散型属性的计算上效果欠佳，需要研究面向含离散型变量多属性案例知识获取的更高效的检索方法；④此外，在多属性诊疗决策案例中，存在着只知道大概范围而不知其准确值的灰数，灰色诊疗决策案例的知识获取问题也是一个值得进一步探索的问题。围绕着以上医院诊疗决策实践中存在的关键科学问题，本研究对传统的案例推理技术进行改进和优化，通过融入统计学、模糊数学、灰色系统理论、遗传算法等其他理论，进一步改进案例推理的性能以适应各种医院诊疗决策问题的实际需要。
具体的研究内容和主要贡献如下：
（1）研究了基于历史案例库的集成开放的智能诊疗决策技术，构建了基于案例推理的智能医院诊疗决策支持系统架构(CBR-based Intelligent Medical Decision Support Systems，CBR-IMDSS)，提出了用改进的案例推理技术辅助决策支持系统的建造，整合DSS和CBR各自的技术优势进行医疗知识挖掘，可以面向疾病早期探测、诊断、预测等不同的诊疗过程，为医院诊疗决策者提供有力支持。
（2）在对国内外属性选择方法进行了综述的基础上，探索了医院诊疗决策中案例属性的选择问题。①给出了主成分分析的数学模型和方法，讨论了主成分分析的使用条件和变量相关性检验的方法。使用威斯康星癌症诊断数据进行测试，通过Barlett球型检验以及特征向量计算等计算过程获取了三个主成分。结果表明，该方法可以降低数据维度；②研究了逻辑回归这种统计分析方法在医疗诊断决策案例权重获取中的应用，给出了相应的理论和模型，采用美国威斯康星州医疗诊断数据和UCI乳腺癌照相肿块数据进行实验，获取了各个特征属性的权重值；③研究了基于信息熵法的权重获取算法，基于UCI乳腺癌照相肿块数据的实验表明信息熵法可以用于权重的获取，而且比常见的德尔菲法有较为明显的优势。
（3）针对医院诊断决策案例中同时含区间模糊数、空间方位数、连续性数值等的情境，探索了基于案例推理的模糊多属性决策方法，提出了一种适应模糊多属性知识获取需求的混合案例检索算法，构建了面向模糊多属性医院诊疗预测的案例推理方法框架，研究不同类型案例知识的表达，重点解决含符号属性、逻辑值属性、二维空间方位属性、程度上有差异（有大小差异）的区间模糊属性等复杂属性案例的检索问题。根据模糊诊疗决策案例的特点并结合医院的实际情况，对传统基于欧氏距离最近邻算法进行了部分修改，以适应空间方位变量的检索计算，形成了面向空间模糊数计算的改进最近邻法IKNN-CSFV。针对有些医院诊疗决策案例中存在的有大小差异区间模糊数的特征属性，将模糊集的概念融入进来，将案例推理问题转化成模糊多属性决策问题，形成面向区间模糊属性案例检索算法FCA-PSIS。整合欧氏最近邻算法、IKNN-CSFV、FCA-PSIS，可以获得适用于医院诊疗决策案例特征的模糊混合案例检索算法FHRA-M。基于Columbia Saint Mary’s Cancer Dataset的实验验证了该方法的有效性；进一步地，与KNN、RBFNetwork、J48等其他几种方法的对比实验表明该方法具有更高的性能。
（4）研究了非连续性属性为主体的医院诊疗决策案例。将条件概率和GAs融合技术整合到案例推理方法之中形成了CRMGACP算法，该算法主要包括基于GAs的权重获取算法和融合条件概率的改进相似度算法。该方法可以被看出KNN法的延伸，相对于传统的KNN法，连续性的自变量保持不变但逻辑变量被延展到所有的离散变量。同时，连续性属性的相似度计算方法基于欧式距离算法但离散型的基于条件概率理论。用VC++实现了一个名为CancerCBRSys的程序，用基于AH肿瘤数据集分别实证研究了固定权重下的KNN（const. w）、专家评价权重下的KNN（expert. W）、CRMGACP以及信息熵法与条件概率的混合案例检索算法CRM-IECP这四种案例推理方法的性能，选择的评价统计量为准确性、灵敏度、特异性和F值。结果显示，CRMGACP在统计的准确性，灵敏度，精密度和F值上都具有最佳性能。分别选择Naive bayes，Logistics，RBF network和Simple Cart来测试相同的测试集和参考集进行比较实验，结果显示CBR（CRM-GACP）的测试结果比其他方法具有更好的性能。总的来说，CRM-GACP在对比试验中展示出更为显著的优势，它可以同时进行连续型和离散型属性值的直接计算而无需离散化连续属性值，是一个可以为临床诊断决策提供支持的具有前景的工具。
（5）探索了灰色诊疗案例中的知识挖掘问题，将信息熵和灰色理论进行了融合，研究了集成优化型灰色理论的案例推理方法。基于UCI mammography数据集的实验表明，就最终准确性而言，通过信息熵可以获得比德尔菲法更高的精度。特别地，在四种不同的融合中，灰色系统理论和信息熵的融合算法获得了最好的效果。其后，是信息熵和基于欧氏距离算法传统最近邻法的融合算法。信息熵和灰色理论的融合算法带来了最好的灵敏度。其他三种融合的敏感度欠佳，也不稳定。几乎所有这些融合的算法在K=1时特异性都最差，但敏感性却不是最差的。总体上讲，在本研究中的乳腺癌诊疗决策中，信息熵法在权重获取方面表现得比德尔菲法更好。综合考虑各种性能，整合信息熵和灰色理论的案例推理方法显得更为优越。（5）通过实证分析方法研究了KNN中K值的选择。从理论上讲，目标案例的最佳匹配对象应该是那个最相似的案例，而不是第二（或第三，或更后的）案例。也就是说，精度应当在k取1时为最高水平。第六章面向诊断决策的灰色案例推理中研究了k取值变化对于检索精度的影响，考察了k值从奇数1变化到奇数13过程中检索精度的相应变化。实验结果表明，当k=1时，精度并不位于最高点。
The knowledge requirement of hospital diagnosis and treatment decision making on theknowledge has typical features which are consistant to the advantage of the case-based reasoningtechnology. However, due to the diversity, variability, complexity, uncertainty and dynamicevolution characteristics of of human illness, as well as the development and change from medicaltechnology and diagnostics, the existing case-based reasoning techniques are unable to meet therequirements of specific problem-solving in medical diagnosis and treatment decision making.Concerning key scientific issues in medical diagnostic decision-making around, the traditionalcase-based reasoning technology was improved and optimized for a more powerful CBR systemwith higher performance to adapt to a variety diagnosis and treatment decision-makingrequirements via the integration of statistical, fuzzy, gray system theory, genetic algorithms andother relevant theories. Then, CBR-IMDSS, an intelligent decision support system frameworkbased on advanced CBR was built and a improved CBR-based were used for the construction ofmedical decision support system which integrates the technical advantages from DSS and CBRrespectively for medical knowledge discovery. CBR-IMDSS can be used to assist the decisionmaking for the physicians during the process of early detection, diagnosis, prediction of a varietyof diseases. In addition to the integration of an open technical framework for intelligent treatmentdecisions based on the history case base, other related specific research content and the maincontributions are as follows.
（1）The intelligent integrating and open medical decision support technology based oncase-based reasoning was explored and CBR-based Intelligent Medical Decision SupportSystems(CBR-IMDSS) was developed. The improved case-based reasoning was presented toconstruct medical decision support system. This technology combined the advantages of DSS andCBR for the data mining of medical case knowledge and can be used for early detection, diagnosis,prognosis and other medical decision process.
(2) The studies with respect to the feasures’ reduction methods were reviewed and then thefeasure reduction of cases for clinical decision making cases in hospitals was examined.①theprinciple component analysis(PCA) and its mathematical models and methods were presented andits necessary conditions for use and the correlation test methods among variables were discussed.Wisconsin cancer data set was used for empirical tests. After the Barlett test, the calculation offeasure vectors and other computing processes, three main components were abtained. It showedthat PCA can reduce the data dimension.②Logistic regression, a common approach ofstatistical analysis and its applications in weight determination of the case in medical diagnosticdecision-making, and the corresponding theories and models were presented. Using UCI breastcancer data, we conducted an experiment and obtained the weights of feasures;③Three different approaches based on information entropy method for weight acquisition algorithm werestudied. The subsequent experiment showed that the entropy method can be used to weight theacquisition, and had better performance than that of Delphi expert weighting method.
(3) In view of a kind of special clinical diagnosis issues in the hospitals, a fuzzymulti-attribute decision-making method based on case-based reasoning was studied. We exploreda comprehensive case retrieval method for fuzzy multi-attribute knowledge acquision requirementwas presented and multi-attribute fuzzy case-based reasoning framework for multi-attribute fuzzyprognosis was built. Different type of knowledge representation was examined and the complexcase retrieval problems with with symbolic attributes, the logic values, and two-dimensionalspatial values were addressed. According to the characteristics of fuzzy decision making cases, aswell as actual situation of hospitals, we performed some modifications on traditional nearestneighbor algorithm based on Euclidean distance to adapt the retrieval needs of spatial orientationvariables, and formed IKNN-CSFV, an improved algorithm for computing fuzzy spacial variables.In terms of fuzzy intervals among characteristic attribute values of hospital diagnosis andtreatment decision making cases, by integrating the concept of fuzzy sets into case-basesreasoning, we transformed the problem of case-based reasoning into fuzzy multi-attributedecision-making and developed FCA-PSIS algorithm for the calculation of interval fuzzy attributevariables in cases. Integrating the nearest neighbor algorithm based on Euclidean distance,IKNN-CSFV, FCA-PSIS, we can obtain FHRA-M method, a comprehensive fuzzy case retrievalalgorithm which is suitable for the characteristics of hospital diagnosis and treatmentdecision-making. Based on Columbia Saint Mary's Cancer Dataset, we empirically completed theeffectiveness validation of this method. Further, we empirically compared KNN, RBFNetwork,J48, and other relevant methods. The result showed FHRA-M has higher performance.
(4) Knowledge acquisition of another kind of special diagnosis and treatment decisionmaking cases in which non-continuous feasures are dominant was studied. We integratedconditional probability and GAs into case-based reasoning technology and developed CRMGACPalgorithm which includes a GAs-based weight determation method and an improved similarityalgorithm integrating the conditional probability. CRMGACP method can be seen as an extensionmethod of traditional KNN. Compared to traditional KNN, its continuitious independent variablesremain the same but the logic variables are extended to all discrete ones. The computing ofcontinutious variables is based on the similarity algorithm based on Euclidean distance but thecalculation of discrete variables is based on conditional probability theory. Using VC++weimplemented a program called CancerCBRSys. Based on Anhui-based cancer data sets, weempirically compared the performance of four different case-based reasoning methods, KNNunder fixed weight (const. w), KNN under the weight from expert evaluation (expert. W),CRMGACP and CRM-IECP, a fixed case retrieval algorithm based on information Entropy andthe conditional probability, respectively. The selected statistics for performance evaluation of algorithms are accuracy, sensitivity, specificity and F-values. The results showed the CRMGACPhas the best performance in accuracy, sensitivity, precision and F-values. Naive bayes, Logistics,RBF network and the Simple Cart were selected to compare the performance based on the sametesting set and reference set and the results show that CBR (CRMGACP) has better performancethan other methods. In general, CRM-GACP shows significant advantage in comparison trials andis hopeful to be a powerful decision-making tool in clinical diagnosis.
(5) The knowledge mining problem in gray diagnosis and treatment decision making case isexplored. In this study, we integrate information entropy theory and gray theory. The gray theoryis optimized and then integrated into the case reasoning. The knowledge mining of cases withincomplete Information, discrete attributes and point-to-point distance calculation is a kind ofcommom problem in hospital diagnosis and treatment decision-making. Considering differentimportance of attributes, weight is brought into the computing of comparative situation and anadvanced local gray relational algorithm is acquired. The experiment based on UCImammography data set suggests the information entropy can be obtained higher accuracy thanDelphi method. Specially, amongst four different fusion methods, the fusion of gray system theoryand information entropy obtained the best results. The second is the fusion algorithm of theinformation entropy and the nearest neighbor method based on Euclidean distance algorithm. Inaddition, the fusion of information entropy and gray theory gets the best sensitivity. The otherthree fusions have poor sensitivity, as well as weak stability. Another interesting discovery is thatalmost all of these fusion algorithms perform the worst in specificity when K is equal to one, butNOT always in sensitivity. Generally, in this experimental study of breast cancer decision-making,information entropy weight method performs better that Delphi method. Considering a variety ofcomprehensive performance, the integration of information entropy theory and case-basedreasoning method has significant advantage in grey case knowledge mining for decision making.In addition, the selection of K values in the KNN is explored. In theory, it seems tha the bestmatching target case should be the most similar case, not the second (or the third, or even later)case. In other words, accuracy should be the highest level when k=1. In the study of the gray casedecision-making in Chapter VI, we examined the influence from changing k value on the retrievalaccuracy. Subsequent experimental result shows that the accuracy is not at the highest point whenis equal to one.
具体的研究内容和主要贡献如下：
（1）研究了基于历史案例库的集成开放的智能诊疗决策技术，构建了基于案例推理的智能医院诊疗决策支持系统架构(CBR-based Intelligent Medical Decision Support Systems，CBR-IMDSS)，提出了用改进的案例推理技术辅助决策支持系统的建造，整合DSS和CBR各自的技术优势进行医疗知识挖掘，可以面向疾病早期探测、诊断、预测等不同的诊疗过程，为医院诊疗决策者提供有力支持。
（2）在对国内外属性选择方法进行了综述的基础上，探索了医院诊疗决策中案例属性的选择问题。①给出了主成分分析的数学模型和方法，讨论了主成分分析的使用条件和变量相关性检验的方法。使用威斯康星癌症诊断数据进行测试，通过Barlett球型检验以及特征向量计算等计算过程获取了三个主成分。结果表明，该方法可以降低数据维度；②研究了逻辑回归这种统计分析方法在医疗诊断决策案例权重获取中的应用，给出了相应的理论和模型，采用美国威斯康星州医疗诊断数据和UCI乳腺癌照相肿块数据进行实验，获取了各个特征属性的权重值；③研究了基于信息熵法的权重获取算法，基于UCI乳腺癌照相肿块数据的实验表明信息熵法可以用于权重的获取，而且比常见的德尔菲法有较为明显的优势。
（3）针对医院诊断决策案例中同时含区间模糊数、空间方位数、连续性数值等的情境，探索了基于案例推理的模糊多属性决策方法，提出了一种适应模糊多属性知识获取需求的混合案例检索算法，构建了面向模糊多属性医院诊疗预测的案例推理方法框架，研究不同类型案例知识的表达，重点解决含符号属性、逻辑值属性、二维空间方位属性、程度上有差异（有大小差异）的区间模糊属性等复杂属性案例的检索问题。根据模糊诊疗决策案例的特点并结合医院的实际情况，对传统基于欧氏距离最近邻算法进行了部分修改，以适应空间方位变量的检索计算，形成了面向空间模糊数计算的改进最近邻法IKNN-CSFV。针对有些医院诊疗决策案例中存在的有大小差异区间模糊数的特征属性，将模糊集的概念融入进来，将案例推理问题转化成模糊多属性决策问题，形成面向区间模糊属性案例检索算法FCA-PSIS。整合欧氏最近邻算法、IKNN-CSFV、FCA-PSIS，可以获得适用于医院诊疗决策案例特征的模糊混合案例检索算法FHRA-M。基于Columbia Saint Mary’s Cancer Dataset的实验验证了该方法的有效性；进一步地，与KNN、RBFNetwork、J48等其他几种方法的对比实验表明该方法具有更高的性能。
（4）研究了非连续性属性为主体的医院诊疗决策案例。将条件概率和GAs融合技术整合到案例推理方法之中形成了CRMGACP算法，该算法主要包括基于GAs的权重获取算法和融合条件概率的改进相似度算法。该方法可以被看出KNN法的延伸，相对于传统的KNN法，连续性的自变量保持不变但逻辑变量被延展到所有的离散变量。同时，连续性属性的相似度计算方法基于欧式距离算法但离散型的基于条件概率理论。用VC++实现了一个名为CancerCBRSys的程序，用基于AH肿瘤数据集分别实证研究了固定权重下的KNN（const. w）、专家评价权重下的KNN（expert. W）、CRMGACP以及信息熵法与条件概率的混合案例检索算法CRM-IECP这四种案例推理方法的性能，选择的评价统计量为准确性、灵敏度、特异性和F值。结果显示，CRMGACP在统计的准确性，灵敏度，精密度和F值上都具有最佳性能。分别选择Naive bayes，Logistics，RBF network和Simple Cart来测试相同的测试集和参考集进行比较实验，结果显示CBR（CRM-GACP）的测试结果比其他方法具有更好的性能。总的来说，CRM-GACP在对比试验中展示出更为显著的优势，它可以同时进行连续型和离散型属性值的直接计算而无需离散化连续属性值，是一个可以为临床诊断决策提供支持的具有前景的工具。
（5）探索了灰色诊疗案例中的知识挖掘问题，将信息熵和灰色理论进行了融合，研究了集成优化型灰色理论的案例推理方法。基于UCI mammography数据集的实验表明，就最终准确性而言，通过信息熵可以获得比德尔菲法更高的精度。特别地，在四种不同的融合中，灰色系统理论和信息熵的融合算法获得了最好的效果。其后，是信息熵和基于欧氏距离算法传统最近邻法的融合算法。信息熵和灰色理论的融合算法带来了最好的灵敏度。其他三种融合的敏感度欠佳，也不稳定。几乎所有这些融合的算法在K=1时特异性都最差，但敏感性却不是最差的。总体上讲，在本研究中的乳腺癌诊疗决策中，信息熵法在权重获取方面表现得比德尔菲法更好。综合考虑各种性能，整合信息熵和灰色理论的案例推理方法显得更为优越。（5）通过实证分析方法研究了KNN中K值的选择。从理论上讲，目标案例的最佳匹配对象应该是那个最相似的案例，而不是第二（或第三，或更后的）案例。也就是说，精度应当在k取1时为最高水平。第六章面向诊断决策的灰色案例推理中研究了k取值变化对于检索精度的影响，考察了k值从奇数1变化到奇数13过程中检索精度的相应变化。实验结果表明，当k=1时，精度并不位于最高点。
The knowledge requirement of hospital diagnosis and treatment decision making on theknowledge has typical features which are consistant to the advantage of the case-based reasoningtechnology. However, due to the diversity, variability, complexity, uncertainty and dynamicevolution characteristics of of human illness, as well as the development and change from medicaltechnology and diagnostics, the existing case-based reasoning techniques are unable to meet therequirements of specific problem-solving in medical diagnosis and treatment decision making.Concerning key scientific issues in medical diagnostic decision-making around, the traditionalcase-based reasoning technology was improved and optimized for a more powerful CBR systemwith higher performance to adapt to a variety diagnosis and treatment decision-makingrequirements via the integration of statistical, fuzzy, gray system theory, genetic algorithms andother relevant theories. Then, CBR-IMDSS, an intelligent decision support system frameworkbased on advanced CBR was built and a improved CBR-based were used for the construction ofmedical decision support system which integrates the technical advantages from DSS and CBRrespectively for medical knowledge discovery. CBR-IMDSS can be used to assist the decisionmaking for the physicians during the process of early detection, diagnosis, prediction of a varietyof diseases. In addition to the integration of an open technical framework for intelligent treatmentdecisions based on the history case base, other related specific research content and the maincontributions are as follows.
（1）The intelligent integrating and open medical decision support technology based oncase-based reasoning was explored and CBR-based Intelligent Medical Decision SupportSystems(CBR-IMDSS) was developed. The improved case-based reasoning was presented toconstruct medical decision support system. This technology combined the advantages of DSS andCBR for the data mining of medical case knowledge and can be used for early detection, diagnosis,prognosis and other medical decision process.
(2) The studies with respect to the feasures’ reduction methods were reviewed and then thefeasure reduction of cases for clinical decision making cases in hospitals was examined.①theprinciple component analysis(PCA) and its mathematical models and methods were presented andits necessary conditions for use and the correlation test methods among variables were discussed.Wisconsin cancer data set was used for empirical tests. After the Barlett test, the calculation offeasure vectors and other computing processes, three main components were abtained. It showedthat PCA can reduce the data dimension.②Logistic regression, a common approach ofstatistical analysis and its applications in weight determination of the case in medical diagnosticdecision-making, and the corresponding theories and models were presented. Using UCI breastcancer data, we conducted an experiment and obtained the weights of feasures;③Three different approaches based on information entropy method for weight acquisition algorithm werestudied. The subsequent experiment showed that the entropy method can be used to weight theacquisition, and had better performance than that of Delphi expert weighting method.
(3) In view of a kind of special clinical diagnosis issues in the hospitals, a fuzzymulti-attribute decision-making method based on case-based reasoning was studied. We exploreda comprehensive case retrieval method for fuzzy multi-attribute knowledge acquision requirementwas presented and multi-attribute fuzzy case-based reasoning framework for multi-attribute fuzzyprognosis was built. Different type of knowledge representation was examined and the complexcase retrieval problems with with symbolic attributes, the logic values, and two-dimensionalspatial values were addressed. According to the characteristics of fuzzy decision making cases, aswell as actual situation of hospitals, we performed some modifications on traditional nearestneighbor algorithm based on Euclidean distance to adapt the retrieval needs of spatial orientationvariables, and formed IKNN-CSFV, an improved algorithm for computing fuzzy spacial variables.In terms of fuzzy intervals among characteristic attribute values of hospital diagnosis andtreatment decision making cases, by integrating the concept of fuzzy sets into case-basesreasoning, we transformed the problem of case-based reasoning into fuzzy multi-attributedecision-making and developed FCA-PSIS algorithm for the calculation of interval fuzzy attributevariables in cases. Integrating the nearest neighbor algorithm based on Euclidean distance,IKNN-CSFV, FCA-PSIS, we can obtain FHRA-M method, a comprehensive fuzzy case retrievalalgorithm which is suitable for the characteristics of hospital diagnosis and treatmentdecision-making. Based on Columbia Saint Mary's Cancer Dataset, we empirically completed theeffectiveness validation of this method. Further, we empirically compared KNN, RBFNetwork,J48, and other relevant methods. The result showed FHRA-M has higher performance.
(4) Knowledge acquisition of another kind of special diagnosis and treatment decisionmaking cases in which non-continuous feasures are dominant was studied. We integratedconditional probability and GAs into case-based reasoning technology and developed CRMGACPalgorithm which includes a GAs-based weight determation method and an improved similarityalgorithm integrating the conditional probability. CRMGACP method can be seen as an extensionmethod of traditional KNN. Compared to traditional KNN, its continuitious independent variablesremain the same but the logic variables are extended to all discrete ones. The computing ofcontinutious variables is based on the similarity algorithm based on Euclidean distance but thecalculation of discrete variables is based on conditional probability theory. Using VC++weimplemented a program called CancerCBRSys. Based on Anhui-based cancer data sets, weempirically compared the performance of four different case-based reasoning methods, KNNunder fixed weight (const. w), KNN under the weight from expert evaluation (expert. W),CRMGACP and CRM-IECP, a fixed case retrieval algorithm based on information Entropy andthe conditional probability, respectively. The selected statistics for performance evaluation of algorithms are accuracy, sensitivity, specificity and F-values. The results showed the CRMGACPhas the best performance in accuracy, sensitivity, precision and F-values. Naive bayes, Logistics,RBF network and the Simple Cart were selected to compare the performance based on the sametesting set and reference set and the results show that CBR (CRMGACP) has better performancethan other methods. In general, CRM-GACP shows significant advantage in comparison trials andis hopeful to be a powerful decision-making tool in clinical diagnosis.
(5) The knowledge mining problem in gray diagnosis and treatment decision making case isexplored. In this study, we integrate information entropy theory and gray theory. The gray theoryis optimized and then integrated into the case reasoning. The knowledge mining of cases withincomplete Information, discrete attributes and point-to-point distance calculation is a kind ofcommom problem in hospital diagnosis and treatment decision-making. Considering differentimportance of attributes, weight is brought into the computing of comparative situation and anadvanced local gray relational algorithm is acquired. The experiment based on UCImammography data set suggests the information entropy can be obtained higher accuracy thanDelphi method. Specially, amongst four different fusion methods, the fusion of gray system theoryand information entropy obtained the best results. The second is the fusion algorithm of theinformation entropy and the nearest neighbor method based on Euclidean distance algorithm. Inaddition, the fusion of information entropy and gray theory gets the best sensitivity. The otherthree fusions have poor sensitivity, as well as weak stability. Another interesting discovery is thatalmost all of these fusion algorithms perform the worst in specificity when K is equal to one, butNOT always in sensitivity. Generally, in this experimental study of breast cancer decision-making,information entropy weight method performs better that Delphi method. Considering a variety ofcomprehensive performance, the integration of information entropy theory and case-basedreasoning method has significant advantage in grey case knowledge mining for decision making.In addition, the selection of K values in the KNN is explored. In theory, it seems tha the bestmatching target case should be the most similar case, not the second (or the third, or even later)case. In other words, accuracy should be the highest level when k=1. In the study of the gray casedecision-making in Chapter VI, we examined the influence from changing k value on the retrievalaccuracy. Subsequent experimental result shows that the accuracy is not at the highest point whenis equal to one.
引文
[1]Fenton, Joshua J.,MD, MPH, et al.,”Influence of Computer-Aided Detection on Performanceof Screening Mammography,” The New England Journal of Medicine, Vol.356, No.14, April5,2007, pp.1399-1409.
[2] Campadelli P, Casiraghi E, Artioli D. A fully automated method for lung nodule detectionfrom postero-anterior chest radiograph. IEEE Trans Med imaging,2006,25:1588-1603.
[3] Chen H, Wang XH, Ma DQ. Neural network-based computer-aided diagnosis indistinguishing malignant from benign solitary pulmonary nodules by computer tomography.Chin Med J,2007,120:1211-1215.
[4] Janet Kolodner, Reconstructive Memory: A Computer Model, Cognitive Science7(1983):4.
[5] Michael Lebowitz, Memory-Based Parsing, Artificial Intelligence21(1983),363-404.
[6] B.Porter, R.Bareiss, Robert Holte.Concept Learning and Heuristie Classifieation In WeakTheory Domains. ArtifieialIntelligence,1990,45:229-263.
[7] A.Aamodt, E.Plaza.Case-basedR easoning:Foundational Issue, Methodological Variation, andSystem Approaehes. Artificial Intelligenence Communication. IOS Press,1994,7(1):39-59.
[8] I.Gilboa, D.Sehaeidler. Case-based Decision Theory.Quarterly Journal of Economies,1995,110:605-639.
[9] F.Gebhardt, A.Vob, W.Grather, et al.Reasoning with complex Cases.Kluwer AcademicPublishers,1997.
[10] L. Maeedo, A. Cardoso. Nested Graph-Strueture Representations for Cases[A]. Europeanworkshop on Case-Based Reasoning’98[C].1998,1-11.
[11] Andres F.M. Rodriguez, Sunil Vadera.PEBM: A Probabillstic Exemplar Based model[A].International Joint Conference on Artifieial Intelligence [C],1999,242-247.
[12] Chiu, C. A case-based customer classification approach for direct marketing. ExpertSystemswith Applications2002,22:163-168.
[13]林闯.基于案例推理系统的Petri网模型.计算机学报,1994,计算机学报,17(A00):77-81.
[14] Bill Mark,"Case-Based Reasoning for Autoclave Management," Proceedings of theCase-Based Reasoning Workshop (1989).
[15] Trung Nguyen, Mary Czerwinski, and Dan Lee,"COMPAQ QuickSource: Providing theConsumer with the Power of Artificial Intelligence," in Proceedings of the Fifth AnnualConference on Innovative Applications of Artificial Intelligence (Washington, DC: AAAI Press,1993),142-151.
[16] Fenggang LI, Zhiwei NI.Case-Based Reasoning Based on Tabu Seareh[A].Proc.3rdIntl.Conf.on Maehine Learning and Cyberneties[C].Shanghai, August2004,2167-2171.
[17] Zhijei Ni, ShanLinYang, Yun Yang, Fenggang Li. Case-based Reasoning Framework. Basedon DataMining Teehnique[A].Proe.3th Intl.Conf.on Maehine Learning and Cyberneties[C].Shanghai, August2004,2511-2514.
[18] Zhiwei Ni, YuLiu, Fenggang Li. Case-based maintenanee based on outlier datamining[A].Proe.4th Intl.Conf.on Machine Learning and Cyberneties[C], Guangzhou, August,2005.
[19]梁昌勇,顾东晓,李兴国,杨善林.面向不确定多属性决策问题的范例检索算法研究[J].中国管理科学,2009,17(1):131-137.
[20]顾东晓,李兴国,梁昌勇,李锋刚.案例检索及权重优化方法研究及应用[J].系统工程学报,2009,24(6):764-768.
[21]于跃海,郑瑞强.基于案例推理的ICU应急诊断系统.系统工程理论与实践,2002,22(3):137-142.
[22]基于案例和模糊推理的农业虫害专家系统研究.计算机工程与设计.2007,28(22):5570-5572.
[23]杨健,马小兰.基于案例推理的中医诊疗专家系统.计算机工程,2008,34(21):178-180.
[24]陈晓红.决策支持系统理论和应用.北京：清华大学出版社，2000
[25] Helmush F. Orthner：http://homepage.uab.edu/horthner/scamc.htm
[26]崔雷,侯跃芳,张晗.论医疗决策支持系统[J].医学情报工作,2003,(1):28-40.
[27]赵起超.论医疗决策支持系统[J].中国医院管理,2001,21(244):40-41.
[28]王津涛,姚磊,姜恩海.基于SPSS的医疗统计决策支持系统设计与实现[J].计算机工程与设计,28（7）：1713-1715.
[29] J. Lieber and B. Bresson. Case-Based Reasoning for Breast Cancer Treatment DecisionHelping. In E. Blanzieri and L. Portinale, editors, Advances in Case-Based Reasoning-Proceedings of the fifth European Workshop on Case-Based Reasoning (EWCBR-2k), LNAI,1898, pages173–185. Springer,2000.
[30]刘兴华,蔡从中,袁前飞,等.基于支持向量机的乳腺癌辅助诊断.重庆大学学报,2007,30(6):140-144.
[31]范逢曦,张海,卢轶郎等.急性心肌梗塞急性期预后专家系统的研究[J].中国生物医学工程学报,1992,11(1):9-16.
[32]刘自伟.常见内科疾病中医诊疗专家辅助系统的设计及其实现[J].计算机时代,1994,(1):1-6.
[33]林东,邵军力.医学诊疗领域通用专家系统设计与实现[J].自动化学报,1995,21(3):380-382.
[34]花蕾等.基于知识的肺癌早期细胞诊断系统[J].计算机应用研究,2000,17(2):90-92.
[35]赵卫东,盛昭瀚,杜雪寒.基于神经网络的案例推理医疗诊断[J].东南大学学报,2000,30(3):46-50.
[36]徐宁,王宽全,张大鹏.基于神经网络算机应用研究,2001,18(2):4-6.
[37]王晶,卫金茂,由军平.支持向量机及其在癌症诊断中的应用[J].计算机工程与应用,2005,（36）：220-222.
[38]李凡,蔡立晶,田应忠.基于Vague集的医疗诊断系统.华中科技大学学报(自然科学版),2002,30(10):47-49.
[39]赵卫东,盛昭瀚.基于形象思维的医疗诊断系统研究[J].系统工程理论与实践,2000,20(10):108-113.
[40]邓锋,符云清,宋锦璘,等. OCDES：口腔癌医疗诊断专家系统设计与实现,计算机科学,2004；31(5):156-158.
[41]老年痴呆症诊断临床决策支持系统设计与评估[J].中国生物医学工程学报,2009,28(6)：872-877.
[42]张华,王崇骏,叶玉坤,陈世福. SARSES:SARS医疗辅助诊断专家系统的设计和实现[J];计算机工程与应用;2004,(18):217-220.
[43]马英豪李沪建徐勇勇孙金杰.军队卫生辅助决策信息系统研发与应用[J].解放军医院管理杂志,2009,16(8):748-750.
[44]李锋刚.基于案例推理的优化型智能决策技术的研究[D],合肥工业大学博士学位论文,2007.
[45] Acorn, T., and Walden, S., SMART: Support management automated reasoning technologyfor Compaq customer service. In Proceedings of the Tenth National Conference on ArtificialIntelligence. MIT Press.(1992).
[46] Hinkle, D., and Toomey, C. N., CLAVIER: Applying case-based reasoning on to compositepart fabrication. Proceeding of the Sixth Innovative Application of AI Conference, Seattle, WA,AAAI Press,(1994). pp.55-62.
[47] William M. Bain Judge: a case-based reasoning system Machine learning. Published inBook: a guide to current research, Kluwer Academic Publishers Norwell, MA, USA1986ISBN:0-89838-214-9.
[48] Cheetham, W., Tenth Anniversary of Plastics Color Matching, Artificial IntelligenceMagazine, Volume26, No.3,(2005). pp51–61.
[49] Callan,James P. CABOT: An Adaptive Approach to Case-Based Search. IJCAI'1991.pp.803-809
[50] Althoff, Klaus-Dieter, Ralph Bergmann, and L. Karl Branting, eds. Case-Based ReasoningResearch and Development: Proceedings of the Third International Conference on Case-BasedReasoning. Berlin: Springer Verlag,1999.
[51] Wittgentstein, L.(1953). Philosophical Investigations. Blackwell.
[52] Aamodt, A.&Plaza, E.(1994). Case-Based Reasoning: Foundational Issues,Methodological Variations, and System Approaches. AI Communications,7(i): pp39-59.
[53] Janet Kolodner, Reconstructive Memory: A Computer Model, Cognitive Science7(1983):4.
[54] Michael Lebowitz,"Memory-Based Parsing," Artificial Intelligence21(1983),363-404.
[55] Kolodner, J. L.(1983b). Reconstructive Memory: A Computer Model. Cognitive Science,7(iv): pp.281-28.
[56] Simpson, R. L.(1985). A Computer Model of Case-Based Reasoning in Problem Solving:An Investigation in the Domain of Dispute Mediation. Technical Report GIT-ICS-85/18, GeorgiaInstitute of Technology, School of Information and Computer Science, Atlanta,US.
[57] Hammond, K.J.(1986). CHEF: A Model of Case-Based Planning. In Proc. AmericanAssociation for Artificial Intelligence, AAAI-86, August1986. Philadelphia, PA, US.
[58] Sycara, E. P.(1987). Resolving adversial conflicts: An approach to Integrating Case-Basedand Analytic Methods. Technical Report GIT-ICS-87/26, Georgia Institute of Technology, Schoolof Information and Computer Science, Atlanta GA.
[59] Koton. P.(1989). Using experience in learning and problem solving. Massachusetts Instituteof Technology, Laboratory of Computer Science, Ph.D. Thesis MIT/LCS/TR-441.
[60] Hinrichs, T.R.(1992). Problem solving in open worlds. Lawrence Erlbaum Associates.
[61] Porter, B.W.&Bareiss, E.R.(1986). PROTOS: An experiment in knowledge acquisition forheuristic classification tasks. In Proceedings of the First International Meeting on Advnces inLearning (IMAL), Les Arcs, France, pp.159-74.
[62] Bareiss, E. R.,(1988). PROTOS: A Unified Approach to Concept Representation,Classification, and learning. Ph.D. thesis, Department. of Computer Science, University ofTexas.
[63] Branting, K.(1991). Exploiting the complementarity of rules and precedents withreciprocity and fairness. In, Proceedings of the Case-Bases Reasoning Workshop1991,Washington, DC, May1991. Sponsored by DARPA. Morgan Kaufmann, pp.39-50.
[64] Ashley, K.D.(1988). Arguing by Analogy in Law: A Case-Based Model. In D.H. Helman(Ed.), Analogical Reasoning: Perspectives of Artificial Intelligence, Cognitive Science, andPhilosophy. D. Reidel.
[65] Reisbeck, C.K,&Schank, R.C.(1989). Inside Case-Based Reasoning. Lawrence ErlbaumAssociates, Hillsdale, NJ, US.
[66] Sharma, S.&Sleeman, D.(1988). REFINER: A Case-Based Differential Diagnosis Aide forKnowledge Acquisition and Knowledge Refinement. In, EWSL88; Proc. European WorkingSession on Learning: pp201-10.
[67] Keane, M.,(1988). Where's the Beef? The absence of pragmatic factors in theories ofanalogy. In, ECAI-88: pp.327-32.
[68] Watson, I.D.,&Abdullah, S.(1994). Developing Case-Based Reasoning Systems: A CaseStudy in Diagnosing Building Defects. In, Proc. IEE Colloquium on Case-Based Reasoning:Prospects for Applications, Digest No:1994/057, pp.1/1-1/3.
[69] Yang, S.,&Robertson, D.(1994). A case-based reasoning system for regulatory information.In, Proc. IEE Colloquium on Case-Based Reasoning: Prospects for Applications, Digest No:1994/057, pp.3/1-3/3.
[70] Moore, C.J., Lehane, M.S.&Proce, C.J.(1994). Case-Based Reasoning for DecisionSupport in Engineering Design. In, Proc. IEE Colloquium on Case-Based Reasoning: Prospectsfor Applications, Digest No:1994/057, pp.4/1-4/4.
[71] Althoff, K.D.(1989). Knowledge acquisition in the domain of CBC machine centres: theMOLTKE approach. In, EKAW-89, Third European Workshop on Knowledge-Based Systems,Boos, J., Gaines, B.&Ganascia, J.G.(eds.), pp.180-95. Paris, July1989.
[72] Richter, A.M.&Weiss, S.(1991). Similarity, uncertainty and case-based reasoning inPATDEX. In, Automated reasoning, essays in honour of Woody Bledsoe. Kluwer R.S. Boyer(ed.): pp249-265.
[73] Oxman, R.E.,(1993a). PRECEDENTS: Memory structure in design case libraries. InCAAD Futures93, Elsevier Science Publishers.
[74] Oxman, R.E.,(1993b). Case-based design support: Supporting architectural compositionthrough precedent libraries. Journal of Architectural Planning Research.
[75] Venkatamaran, S., Krishnan, R.&Rao, K.K.(1993). A rule-case based system for imageanalysis. In, Proc.1st. European Workshop on Case-Based Reasoning, Posters&Presentations,2:pp.410-15.
[76] Isabelle Bichindaritz，Cindy Marling. Case-based reasoning in the health sciences: What'snext?，Artificial Intelligence in Medicine2006;36(2):127-135.
[77] Stefania Montani，Exploring new roles for case-based reasoning in heterogeneous AIsystems for medical decision support，Applied Intelligence2008;28(3):275-285.
[78] Janet Kolodner, Case-based reasoning, Morgan Kaufmann Publishers Inc., San Francisco,CA,1993.
[79] Monica H. Ou, Geoff A.W. West, Mihai Lazarescu, Chris Clay. Dynamic knowledgevalidation and verification for CBR teledermatology system, Artificial Intelligence in Medicine,Volume39, Issue1,2007, Pages79-96.
[80] Hui Li, Jie Sun. Hybridizing principles of the Electre method with case-based reasoning fordata mining: Electre-CBR-I and Electre-CBR-II, European Journal of Operational Research,Volume197, Issue1,2009, Pages214-224.
[81] Monique Frize, Robin Walker. Clinical decision-support systems for intensive care unitsusing case-based reasoning, Medical Engineering&Physics, Volume22, Issue9,2000, Pages671-677
[82] Marion C.J. Biermans, Dinny H. de Bakker, Robert A. Verheij, Jan V. Gravestein, Michiel W.van der Linden, Pieter F. de Vries Robbé. Development of a case-based system for groupingdiagnoses in general practice, International Journal of Medical Informatics, Volume77, Issue7,2008, Pages431-439.
[83] Chien-Chang Hsu, Cheng-Seen Ho. A new hybrid case-based architecture for medicaldiagnosis, Information Sciences, Volume166, Issues1-4,2004, Pages231-247
[84] Syed Sibte Raza Abidi, Selvakumar Manickam. Leveraging XML-based electronic medicalrecords to extract experiential clinical knowledge: An automated approach to generate cases formedical case-based reasoning systems, International Journal of Medical Informatics, Volume68,Issues1-3,2002, Pages187-203.
[85] Rainer Schmidt, Lothar Gierl. A prognostic model for temporal courses that combinestemporal abstraction and case-based reasoning, International Journal of Medical Informatics,Volume74, Issues2-4,2005, Pages307-315.
[86] Amjad Waheed, Hojjat Adeli. Case-based reasoning in steel bridge engineering.Knowledge-Based Systems, Volume18, Issue1,2005, Pages37-46.
[87] Chi-man Vong, Pak-kin Won. Case-based adaptation for automotive engine electroniccontrol unit calibration. Expert Systems with Applications, Volume37, Issue4,2010, Pages3184-3194.
[88] Kefeng Hua, Boi Fairings, Ian Smith. CADRE: case-based geometric design. ArtificialIntelligence in Engineering, Volume10, Issue2,1996, Pages171-183
[89] Atish P. Sinha and Jerrold H. May.Providing Design Assistance: A Case-Based Approach.Information Systems Research,1996;7:363-387.
[90] Kyung-shik Shin, Ingoo Han. A case-based approach using inductive indexing for corporatebond rating. Decision Support Systems, Volume32, Issue1,2001, Pages41-52
[91] Chaochang Chiu. A case-based customer classification approach for direct marketing.Expert Systems with Applications, Volume22, Issue2, February2002, Pages163-168
[92] Kamalendu, Owen. A decision support system for business acquisitions. Decision SupportSystems, Volume27, Issue4,2000, Pages411-429.
[93] Günter Schmidt. Case-based reasoning for production scheduling. International Journal ofProduction Economics, Volumes56-57,1998, Pages537-546.
[94] Y.F. Li, M. Xie, T.N. Goh. A study of mutual information based feature selection for casebased reasoning in software cost estimation.Expert Systems with Applications, Volume36, Issue3, Part2,2009, Pages5921-5931.
[95] Mukhopadhyay, T., Vicinanaza, S.S, Prieutula, M.J.(1992). Examining the feasibility of acase-based reasoning model for software effort estimation. MIS Quarterly,16(2), pp.155-72.
[96] Robert D. Austin and Lee Devin. Redesigning Case Retrieval to Reduce InformationAcquisition Costs. Information Systems Research,1997;8:51-68.
[97] Robert D. Austin and Lee Devin. Research Commentary—Weighing the Benefits and Costsof Flexibility in Making Software: Toward a Contingency Theory of the Determinants ofDevelopment Process Design. Information Systems Research,2009;20:462–477
[98] Abdus Salam Khan, Achim Hoffmann. Building a case-based diet recommendation systemwithout a knowledge engineer, Artificial Intelligence in Medicine, Volume27, Issue2,2003,Pages155-179.
[99] Raquel Ros, Josep Lluís Arcos, Ramon Lopez de Mantaras, Manuela Veloso. A case-basedapproach for coordinated action selection in robot soccer. Artificial Intelligence, Volume173,Issues9-10,2009, Pages1014-1039.
[100] Ting-Peng Liang. Analogical reasoning and case-based learning in model managementsystems. Decision Support Systems, Volume10, Issue2,1993, Pages137-160
[101] Stephan Grolimund, Jean-Gabriel Ganascia. Driving Tabu Search with case-basedreasoning, European Journal of Operational Research, Volume103, Issue2,1997, Pages326-338
[102] Hamid R. Nemati, David M. Steiger, Lakshmi S. Iyer, Richard T. Herschel. Knowledgewarehouse: an architectural integration of knowledge management, decision support, artificialintelligence and data warehousing, Decision Support Systems, Volume33, Issue2,2002, Pages143-161.
[103] Yong Sik Chang, Jae Kyu Lee. Case-based modification for optimization agents:AGENT-OPT, Decision Support Systems, Volume36, Issue4,2004, Pages355-370.
[104] Vasyl Golosnoy, Yarema Okhrin. General uncertainty in portfolio selection: A case-baseddecision approach. Journal of Economic Behavior&Organization, Volume67, Issues3-4,2008,Pages718-734.
[105] Heng Li. Case-based reasoning for intelligent support of construction negotiation.Information&Management, Volume30, Issue5,1996, Pages231-238.
[106] Lyle Brenner, Dale Griffin, Derek J. Koehler. Modeling patterns of probability calibrationwith random support theory: Diagnosing case-based judgment. Organizational Behavior andHuman Decision Processes, Volume97, Issue1,2005, Pages64-81.
[107] Matthias Blonski. Social learning with case-based decisions, Journal of EconomicBehavior&Organization, Volume38, Issue1,1999, Pages59-77.
[108] Elisabet Golobardes, Xavier Llorà, Maria Salamó, Joan Martí. Computer aided diagnosiswith case-based reasoning and genetic algorithms. Knowledge-Based Systems, Volume15,Issues1-2,2002, Pages45-52.
[109] E.B Reategui, J.A Campbell, B.F Leao. Combining a neural network with case-basedreasoning in a diagnostic system, Artificial Intelligence in Medicine, Volume9, Issue1,1997,Pages5-27.
[110] Zoe Y. Zhuang, Leonid Churilov, Frada Burstein, Ken Sikaris. Combining data mining andcase-based reasoning for intelligent decision support for pathology ordering by generalpractitioners, European Journal of Operational Research, Volume195, Issue3,2009, Pages662-675.
[111] Javier Bajo, Juan F. de Paz, Yanira de Paz, Juan M. Corchado, Integrating case-basedplanning and RPTW neural networks to construct an intelligent environment for health care,Expert Systems with Applications: An International Journal, v.36n.3, p.5844-5858, April,2009
[112] Oh Byung Kwon, Norman Sadeh. An expert system with case-based reasoning for databaseschema design. Decision Support Systems, Volume18, Issue1,1996, Pages83-95
[113] Pei-Chann Chang, Chien-Yuan Lai, K. Robert Lai. A hybrid system by evolving case-basedreasoning with genetic algorithm in wholesaler's returning book forecasting. Decision SupportSystems, Volume42, Issue3,2006, Pages1715-1729.
[114] Jocelyn San Pedro, Frada Burstein, Alan Sharp. A case-based fuzzy multicriteria decisionsupport model for tropical cyclone forecasting. European Journal of Operational Research,Volume160, Issue2,2005, Pages308-324.
[115] Sheng-Tun Li, Hei-Fong Ho. Predicting financial activity with evolutionary fuzzycase-based reasoning. Expert Systems with Applications, Volume36, Issue1,2009, Pages411-422
[116] F. Faez, S.H. Ghodsypour, C. O’Brien. Vendor selection and order allocation using anintegrated fuzzy case-based reasoning and mathematical programming model, InternationalJournal of Production Economics, Volume121, Issue2,2009, Pages395-408
[117] Oh Byung Kwon, Norman Sadeh. Applying case-based reasoning and multi-agentintelligent system to context-aware comparative shopping, Decision Support Systems, Volume37, Issue2,2004, Pages199-213.
[118] Aasia Khanum, Muid Mufti, M. Younus Javed, M. Zubair Shafiq. Fuzzy case-basedreasoning for facial expression recognition. Fuzzy Sets and Systems, Volume160, Issue2,162009, Pages231-250.
[119] K.M. Saridakis, A.J. Dentsoras. Case-DeSC: A system for case-based design with softcomputing techniques, Expert Systems with Applications, Volume32, Issue2,2007, Pages641-657.
[120] A. H. Mohamed, F. A. Mohamed, A. M. Nassar, M. H. El-Fouly. Case-functional-baseddiagnostic system (CFDS), Engineering Applications of Artificial Intelligence, Volume15, Issue5,2002, Pages501-509.
[121] Malrey Lee. A study of an automatic learning model of adaptation knowledge for case basereasoning, Information Sciences, Volume155, Issues1-2,2003, Pages61-78
[122]史忠植.高级人工智能[M].北京:科学出版社,1998.
[123]刘芳,姚莉,王长缨.基于语义Web的案例表示和CBR系统结构研究[J].计算机应用,2004,24(1):17-19.
[124]张月雷,左洪福.基于本体的案例表示和CBR系统结构研究[J].山东理工大学学报:自然科学版,2006,20(4):48-51.
[125]丁剑飞,何玉林,李成武.基于本体的分布式CBR设计系统[J].计算机工程,2007,33(21):183-185.
[126] Aamodt A. Knowledge intensive case-based reasoning in CREEK[C].Proc of the7thEuropean Conference on Case-based Reasoning. Berlin: Spring,2004:291-305.
[127] Diaz-Agudo B, Gonzalez-Calero P A. Knowledge intensive CBR through ontologies[J].Expert Update,2003,6(1):44-54.
[128] Salton. Introduction to Modern Information Retrieval [J]. McGraw-Hill Company,1983,120-130.
[129]陈朝阳,张代胜,任佩红. CBR诊断系统实例获取的合成相似性度量方法[J].机械工程学报,2004,40(5):48-51.
[130] Vong C M,Leung T P,Wong P K.Case-based reasoning and adaptation in hydraulicproduction machine design[J].Engineering Applications of Artificial Intelligence,2002,15:567-585.
[131]汤廷孝,刘勇等. CBR系统中的实例修改研究[J].机械科学与技术,2006,25(4):390-393
[132]Lee M. A study of automatic learning model of adaptation knowledge for case-basedreasoning[J].Information Sciences,2003,155:61-78.
[133] Smyth B,Keane M T.Using adaptation knowledge to retrieve and adapt designcases[J].Knowledge Based System,1996,9:127-135.
[134] Virkki Hatakka T,et al.Adaptation phase in case-based reasoning system for processequipment selection[J].Computers Chemical Engineering,1997,21:643-648.
[135]张斌,高全杰,应保胜.实例推理和规则推理在实例修改中的应用[J].计算机工程,2005,31(13):156-158.
[136] S.L. Chan, W.H. Ip. A dynamic decision support system to predict the value of customerfor new product development. Decision Support Systems, In Press, Corrected Proof, Availableonline2011.
[137] Noel Bryson, Ayodele Mobolurin. An approach to using the analytic hierarchy process forsolving multiple criteria decision making problems. European Journal of Operational Research,Volume76, Issue3,1994, Pages440-454.
[138]胡中辉,李烨,蔡云泽,黄金杰.基于属性约简及支持向量机的医疗诊断决策研究[J].计算机工程与应用,2005,(13):183-185.
[139] Slezak, Dominik,Ziarko,Wojciech. Attibute reduction in Bayesian version of variableprecision rough set model. Electronic Notes in Theoretical Computer Science,2003,82(4):1-11.
[140] Jensen, Richard, Shen, Qiang. Fuzzy-rough attributes reduction with application to webcategorization. Fuzzy Sets and Systems,2004,141(3):469-485.
[141] Kemal Polat, Salih Güne. A hybrid medical decision making system based on principlescomponent analysis, k-NN based weighted pre-processing and adaptive neuro-fuzzy inferencesystem. Digital Signal Processing, Volume16, Issue6,2006, Pages913-921
[142] Engin Avci, Ibrahim Turkoglu. An intelligent diagnosis system based on principlecomponent analysis and ANFIS for the heart valve diseases. Expert Systems with Applications,Volume36, Issue2, Part2,2009, Pages2873-2878
[143] Greene，Derek，Freyne，Jill，et al.(2008). An Analysis of Research Themes in the CBRConference Literature. In Althoff，K.-D.，Bergmann，R.，Minor，M.，Hanft，A.(Eds.)，Advancesin Case-Based Reasoning-Proceedings of the9th European Workshop on Case-Based Reasoning(ECCBR2008)，Trier，Germany，September1-4，18-43. Springer.
[144] Zhang，Lu，Coenen，Frans and Leng，Paul.(2002). Formalising optimal feature weightsetting in case based diagnosis as linear programming problems，Knowledge-Based Systems15(7):391-398.
[145] Renauda, J., Levratb, E. and Fonteixc, C.(2008). Weights determination of OWA operatorsby parametric identification, Mathematics and Computers in Simulation77:499–511.
[146] Schaaf, J.W., Fish and shrink.(1996). A next step towards efficient case retrieval in largescaled case bases, in: Advances in Case-based Reasoning: Third European Workshop, Lausanne,Switzerland,362–377.
[147] Saridakis, K.M., Dentsoras, A.J..(2007). Case-DeSC: A system for case-based design withsoft computing techniques, Expert Systems with Applications32(2):641-657.
[148] Zou, Zhi-hong, Yun, Yi and Sun, Jing-nan.(2006). Entropy method for determination ofweight of evaluating indicators in fuzzy synthetic evaluation for water quality assessment,Journal of Environmental Sciences18(5):1020-1023.
[149] Reategui, E.B, Campbell, J.A, Leao, B.F.(1997). Combining a neural network withcase-based reasoning in a diagnostic system, Artificial Intelligence in Medicine9(1):5–27.
[150] Schmidt R, Gierl L.(2001). Case-based reasoning for antibiotics therapy advice: aninvestigation of retrieval algorithms and prototypes. Art Intelligence in Medicine23(2):171-186.
[151] Singh, M., Mandal, M.K., Basu, A.(2005). Gaussian and Laplacian of Gaussian weightingfunctions for robust feature based tracking, Pattern Recognition Letters26(13):1995–2005.
[152] Tan, Tuan Zea and Quek, Chai.(2008). Ovarian cancer diagnosis with complementarylearning fuzzy neural network, Artificial Intelligence in Medicine43:207-222.
[153] West, David, Mangiameli, Paul, Rampal, Rohit, West, Vivian.(2005). Ensemble strategiesfor a medical diagnostic decision support system: A breast cancer diagnosis application.European Journal of Operational Research162(2):532-551.
[154] E.P. Xing, M.I. Jordan, R.M. Karp, Feature selection for high-dimensional genomicmicroarray data, in: Proc. of the18th International Conference on Machine Learning, pp.601–608,(2001).
[155] J. Friedman, T. Hastie, R. Tibshirani, The elements of statistical learning. Springer,(2001).
[156] Hosmer, D. W., and Lemeshow, S., Applied logistic regression,2nd edition. Wiley, NewYork,2000.
[157]Mammogram Interpretation: Categories and the ACR/BI-RADS,http://www.imaginis.com/breasthealth/acrbi.asp,(2007).
[158] M. Elter, R. Schulz-Wendtland, T. Wittenberg, Mammographic Mass Data Set,http://archive.ics.uci.edu/ml/datasets/Mammographic+Mass,2007
[159] N. Marchettini, R.M. Pulselli, F. Rossi, E. Tiezzi. Entropy, Encyclopedia of Ecology,2008,Pages1297-1305.
[160] Balian, Roger.(2003). Entropy–Protean Concept. Poincaré Seminar2:119-45.
[161] Jaynes E T.(1957). Information theory and statistical mechanics. Physical Review106(4):620-630.
[162] Zhang, Lu, Coenen, Frans and Leng, Paul.(2002). Formalising optimal feature weightsetting in case based diagnosis as linear programming problems, Knowledge-Based Systems15(7):391-398.
[163] Zhuang, Zoe Y., Churilov, Leonid, et al.(2009). Combining data mining and case-basedreasoning for intelligent decision support for pathology ordering by general practitioners,European Journal of Operational Research195(3):662-675.
[164] Tuan Zea Tan, Chai Quek, Geok See Ng, Khalil Razvi. Ovarian cancer diagnosis withcomplementary learning fuzzy neural network, Artificial Intelligence in Medicine,(2008)43,207-222
[165] Isabelle Bichindaritz, Cindy Marling, Case-based reasoning in the health sciences: What'snext?, Artificial Intelligence in Medicine,2006,36(2):127-135.
[166] Stefania Montani, Exploring new roles for case-based reasoning in heterogeneous AIsystems for medical decision support, Applied Intelligence,2008,28(3):275-285.
[167] Markus Nilsson, Peter Funk, Erik M. G. Olsson, Bo von Schéele, Ning Xiong, Clinicaldecision-support for diagnosing stress-related disorders by applying psychophysiologicalmedical knowledge to an instance-based learning system, Artificial Intelligence in Medicine,2006,36(2):159-176.
[168] Marion C.J. Biermans, Dinny H. de Bakker, Robert A. Verheij, Jan V. Gravestein, MichielW. van der Linden, Pieter F. de Vries Robbé. Development of a case-based system for groupingdiagnoses in general practice, International Journal of Medical Informatics2008;77(7):431-439.
[169] Chien-Chang Hsu, Cheng-Seen Ho. A new hybrid case-based architecture for medicaldiagnosis, Information Sciences2004;166(1-4):231-247
[170] A.O. Bilska-Wolak and C.E. Floyd Jr., Development and evaluation of a case-basedreasoning classifier for prediction of breast biopsy outcome with bi-radstm lexicon, MedicalPhysics29(9)(2002),2090-2100.
[171] Juan M. Corchado, Javier Bajo, Yanira de Paz, Dante I. Tapia, Intelligent environmentfor monitoring Alzheimer patients, agent technology for health care, Decision Support Systems,v.44n.2, p.382-396,2008
[172] Cindy Marling, Peter Whitehouse, Case-Based Reasoning in the Care of Alzheimer'sDisease Patients, Proceedings of the4th International Conference on Case-Based Reasoning:Case-Based Reasoning Research and Development, p.702-715,2001
[173] Aquin M, Lieber J, Napoli A (2006) Adaptation knowledge acquisition: a case study forcase-based decision support in oncology. In: Bichindaritz I, Marling C (eds) Special issue onCBR in the health sciences. Comput Intell22(3-4):161-176
[174] Schmidt R, Gierl L, Case-based reasoning for antibiotics therapy advice: an investigationof retrieval algorithms and prototypes. Art Intelligence in Medicine2001;23(2):171-186
[175] Herrera F, Martinez L. An approach for combining numerical and linguistic informationbased on the2-tuple fuzzy linguistic representation model in decision making [J]. InternationalJournal of Uncertainty, Fuzziness and Knowledge-Based Systems,2000,8:539-562.
[176] Li D F, Yang J B. Fuzzy linear programming technique for multi-attribute group decisionmaking in fuzzy Environments [J]. Information Sciences,2004,158:263-275.
[177] Xu Z S. Uncertain linguistic aggregation operators based approach to multiple attributegroup decision making under uncertain linguistic environment [J]. Information Sciences,2004,(168):171-184.
[178] Xu Z. Group decision making with triangular fuzzy linguistic variables[C]//IDEAL2007,LNCS,2007,4881:17-26.
[179] Liang X C, Chen S F. Multiple at tribute decision making metho d based o n trapezoidfuzzy linguistic var iables [J]. Journal of Southeast University (English Edition),2008,24(4):478-481.
[180] Héctor Nú ez, Miquel Sànchez-Marrè, Ulises Cortés, Joaquim Comas, Montse Martínez,Ignasi Rodríguez-Roda, Manel Poch. A comparative study on the use of similarity measures incase-based reasoning to improve the classification of environmental system situations,Environmental Modeling&Software, Volume19, Issue9, September2004, Pages809-819
[181] S. Oliveira, J.F.F. Ribeiro, S.C. Seok. A comparative study of similarity measures formanufacturing cell formation, Journal of Manufacturing Systems, Volume27, Issue1, January2008, Pages19-25
[182] B. De Baets and H. De Meyer, Transitivity-preserving fuzzification schemes forcardinality-based similarity measures, European J. Oper. Res.160(2005), pp.726–740
[183] C.S. Park, I. Han, A case-based reasoning with the feature weights derived by analytic255–264
[184] Feng Kong. Theory, Methods and Applications for Fuzzy Multiple Attribute DecisionMaking. Beijing: China Agricultural Science Press.2008.3
[184] Chen, C. T. Extensions of the TOPSIS for group decision-making under fuzzy environment.Fuzzy Sets and Systems,114(1)(2000),1-9.
[184] Jin Qi, Jie Hu, Ying-hong Peng, Wei-ming Wang, Zhenfei Zhang: A case retrieval methodcombined with similarity measurement and multi-criteria decision making for concurrent design.Expert Syst. Appl.36(7)(2009):10357-10366begin_of_the_skype_highlighting
[187] Ian H. Witten; Eibe Frank (2005)."Data Mining: Practical machine learning tools andtechniques,2nd Edition". Morgan Kaufmann, San Francisco.http://www.cs.waikato.ac.nz/~ml/weka/book.html. Retrieved2007-06-25.
[188] http://en.wikipedia.org/wiki/Sensitivity_and_specificity
[189]American Cancer Society. Breast Cancer Facts&Figures2009-2010(Web Site).http://www.cancer.org/downloads/STT/F861009_final%209-08-09.pdf. February18,2010
[190]American Cancer Society. Breast Cancer Facts&Figures2009-2010(Web Site).http://www.cancer.org/downloads/STT/500809web.pdf. February18,2010
[191]D. Max Parkin, Freddie Bray, J. Ferlay and Paola Pisani. Global Cancer Statistics,2002,CA Cancer J Clin2005;55:74-108
[192]P Hider and B Nicholas, The early detection and diagnosis of breast cancer: literaturereview–an update. New Zealand Health Technology Assessment Clearing House (NZHTA)Report (1999), p.2-32
[193] Yarnold, J. Early and Locally Advanced Breast Cancer: Diagnosis and Treatment NationalInstitute for Health and Clinical Excellence Guideline2009
[194] N. Houssami, S. Ciatto, F. Martinelli, R. Bonardi, and S. W. Duffy, Early detection ofsecond breast cancers improves prognosis in breast cancer survivors, Ann. Onc.,2009;20(9):1505-1510.
[195] Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and prognosis vialinear programming. Operations Research,1995,43(4), pages570-577
[196] T. Ayer, J. Chhatwal, O. Alagoz, C. E. Kahn Jr, R. W. Woods, and E. S. Burnside.Informatics in Radiology: Comparison of Logistic Regression and Artificial Neural NetworkModels in Breast Cancer Risk Estimation, Radio Graphics,2010;30(1):13-22.
[197]Hung et al., M. Hung, M. Shanker and M. Hu, Estimating breast cancer risks using neuralnetworks, Journal of the Operational Research Society53(2002), pp.222–231
[198] J.L. Castro, M. Navarro, J.M. Sánchez, J.M. Zurita. Loss and gain functions for CBRretrieval, Information Sciences, Volume179, Issue11,2009, Pages1738-1750
[199] H. Li, J. Sun, Ranking-order case-based reasoning for financial distress prediction,Knowledge-Based Systems21(8)(2008)868–878.
[200] Hui Li, Jie Sun. Gaussian case-based reasoning for business failure prediction withempirical data in China. Information Sciences, Volume179, Issues1-2,2009, Pages89-108
[201] David West, Paul Mangiameli, Rohit Rampal, Vivian West. Ensemble strategies for amedical diagnostic decision support system: A breast cancer diagnosis application. EuropeanJournal of Operational Research, Volume162, Issue2,2005, Pages532-551
[202]T. Ayer, J. Chhatwal, O. Alagoz, C. E. Kahn Jr, R. W. Woods, and E. S. Burnside.Informatics in Radiology: Comparison of Logistic Regression and Artificial Neural NetworkModels in Breast Cancer Risk Estimation, Radio Graphics2010;30(1):13-22.
[203] Hung et al., M. Hung, M. Shanker and M. Hu. Estimating breast cancer risks using neuralnetworks, Journal of the Operational Research Society2002;53:222–231.
[204] J. Lieber and B. Bresson. Case-Based Reasoning for Breast Cancer Treatment DecisionHelping. In E. Blanzieri and L. Portinale, editors, Advances in Case-Based Reasoning-Proceedings of the fifth European Workshop on Case-Based Reasoning (EWCBR-2k), LNAI,1898, pages173–185. Springer,2000.
[205] C. E. Floyd, Jr., J. Y. Lo, and G. D. Tourassi,"Case-based reasoning computer algorithmthat uses mammographic findings for breast biopsy decisions," Am. J. Roentgenol.175,1347–1352(2000)
[206] A.O. Bilska and C. E. Floyd, Jr.,"Investigating different similarity measures for acase-based reasoning classifier to predict breast cancer," Proc. SPIE Med. Imaging4322,1862–1866(2001)
[207] K.S. Shin, I. Han. Case-based reasoning supported by genetic algorithms for corporatebond rating, Expert Systems with Applications16(1999)85–95.
[208] K.J. Kim, I. Han. Maintaining case-based reasoning systems using a genetic algorithmsapproach, Expert Systems with Applications21(2001)139–145.
[209] C. Chiu. A case-based customer classification approach for direct marketing, ExpertSystems with Applications22(2002)163–168.
[210] Y. Fu, R. Shen. GA based CBR approach in Q&A system, Expert Systems withApplications26(2004)167–170.
[211] Gu DX, Liang CY, Li XG, et al. Intelligent Technique for Knowledge Reuse of DentalMedical Records Based on Case-Based Reasoning. Journal of medical systems2010;34(2):213-222
[212]Brendan McCane, Michael Albert. Distance functions for categorical and mixed variables.Pattern Recognition Letters,2008;29(7):986-993
[213] C. Stanfill and D.L. Waltz,“Toward Memory-Based Reasoning,”Comm. ACM, vol.29, pp.1213-1228,1986.
[214]Gower, J.C.,1971. A general coefficient of similarity and some of its properties. Biometrics27,857–874.
[215]D. Wilson and T. Martinez,“Improved Heterogeneous Distance Functions,” J. ArtificialIntelligence Research, vol.6, pp.1-34,1997.
[216]Derek Greene, Jill Freyne, Barry Smyth, Padraig Cunningham: An Analysis of ResearchThemes in the CBR Conference Literature. ECCBR2008:18-43
[217]J. Renauda, E. Levratb, C. Fonteixc, Weights determination of OWA operators byparametric identification, Mathematics and Computers in Simulation2008;77:499–511
[218] Lu Zhang, Frans Coenen, Paul Leng. Formalising optimal feature weight setting incase-based diagnosis as linear programming problems, Knowledge-Based Systems2002;15(7):391-398.
[219]C.S. Park, I. Han, A case-based reasoning with the feature weights derived by analytichierarchy process for bankruptcy prediction, Expert Systems with Applications2002;23(3):255–264
[220]S.Z. Dogan, D. Arditi, H.M. Gnaydin, Using decision trees for determining attributeweights in a case-based model of early cost prediction, Journal of Construction Engineering andManagement2008;134(2):146–152
[221]M. V. Fidelis, H. S. Lopes, and A. A. Freitas. Discovering Comprehensible ClassificationRules with a Genetic Algorithm. In Proc. Of the2000Congress on Evolutionary Computation,1,pp.805-810,2000.
[222] Goldberg, D.E. Genetic Algorithms in Search, Optimization and Machine Learning.Reading: Addison-Wesley,1989.
[223]Cunningham, P. A taxonomy of similarity mechanisms for case-based reasoning. IEEETransactions on Knowledge and Data Engineering,2008;21(11):1532-1543
[224]D. West, P. Mangiameli, R. Rampal and V. West, Ensemble strategies for a medicaldiagnosis previous termdecisionnext term support system: a breast previous term cancer nextterm diagnosis application, Eur. J. Operat. Res.162(2005), pp.532–551
[225] Sangjae Lee. Using data envelopment analysis and decision trees for efficiency analysisand recommendation of B2C controls. Decision Support Systems, Volume49, Issue4,2010,Pages486-497
[226]RBF network L.M. Salchenberger, E.M. Cinar and N.A. Lash, Neural networks: a new toolfor predicting thrift failures, Decision Sciences23(4)(1992), pp.899–916
[227]RBF network Murat Karabatak, M. Cevdet Ince. An expert system for detection of breastcancer based on association rules and neural network. Expert Systems with Applications, Volume36, Issue2, Part2,2009, Pages3465-3469
[228] RBF network Randall S. Sexton, Robert E. Dorsey. Reliable classification using neuralnetworks: a genetic algorithm and back propagation comparison. Decision Support Systems,Volume30, Issue1,2000, Pages11-22
[229] Zhen Zhang, Hong Zhang, Robert C. Bast Jr. An Application of Artificial Neural Networksin Ovarian Cancer Early Detection, Proceedings of the IEEE-INNS-ENNS International JointConference on Neural Networks (IJCNN'00)-Volume4, p.4107, July24-27,2000
[230] RBF network Tuan Zea Tan, Chai Quek, Geok See Ng, Khalil Razvi, Ovarian cancerdiagnosis with complementary learning fuzzy neural network, Artificial Intelligence in Medicine,v.43n.3, p.207-222, July,2008
[231] RBF network Yuehui Chen, Yan Wang, Bo Yang: Evolving Hierarchical RBF NeuralNetworks for Breast Cancer Detection. ICONIP (3)2006:137-144
[232] Cruz-Ramirez N, Acosta-Mesa HG, Carrillo-Calvet H, et al. Discovering interobservervariability in the cytodiagnosis of breast cancer using decision trees and Bayesian networks.Applied Soft Computing,20099(4):1331-1342
[233] Naive bayes P. Antal, H. Verrelst, D. Timmerman, S. Van Huffel, B. de Moor, I. Vergote,Bayesian Networks in Ovarian Cancer Diagnosis: Potentials and Limitations, Proceedings of the13th IEEE Symposium on Computer-Based Medical Systems (CBMS'00), p.103, June23-24,2000
[234] Logistics Chun-Lang Chang, Ming-Yuan Hsu. The study that applies artificial intelligenceand logistic regression for assistance in differential diagnostic of pancreatic cancer. ExpertSystems with Applications, Volume36, Issue7,2009, Pages10663-10672
[235] I.H. Witten and E. Frank, Data mining: practical machine learning tools and techniques(second ed.), Morgan Kaufmann, San Francisco (2005).
[236]Bichindaritz and C. Marling. Case-based reasoning in the health sciences: What's next?Artificial Intelligence in Medicine,36(2):127–135,2006
[237]Klaus-Dieter Althoff, Ralph Bergmann, et al. Case-based reasoning for medical decisionsupport tasks: The Inreca approach. Artificial Intelligence in Medicine,1998,12(1):25-41
[238]Venkatesh, V., Speier, C.,&Morris, M. G.(2002). User acceptance enablers in individualdecision making about technology: Toward an integrated model. Decision Sciences,33(2),297-316.
[239]Wixom, B. H.,&Todd, P. A.(2005). A theoretical integration of user satisfaction andtechnology acceptance. Information Systems Research,16(1),85-102.
[240]Venkatesh, V.,&Morris, M. G.(2000). Why don't men ever stop to ask for directions?gender, social influence, and their role in technology acceptance and usage behavior. MISQuarterly,24(1),115-139.
[241]Agnar Aamodt and Enric Plaza. Case-Based Reasoning: Foundational Issues,Methodological Variations, and System Approaches. Artificial Intelligence Communications, IOSPress7(1)(1994)39-59.
[242]Ramon López de Mántaras, David McSherry, Derek Bridge, David Leake, Barry Smyth,Susan Craw, Boi Faltings, Mary Lou Maher, Michael Cox, Kenneth Forbus, Mark Keane, AgnarAamodt, and Ian Watson. Retrieval, reuse, revision, and retention in case-based reasoning.Knowledge Engineering Review. Cambridge University Press20(3)(2005)215-240.
[243] Bichindaritz, I., Marling, C., Case-based Reasoning in the Health Sciences: What’s Next?,Artificial Intelligence in Medicine, Special Issue on Case-based Reasoning in the HealthSciences, Bichindaritz, I.(Edt.)36(2)(2006)127-135.
[244] Peide Liu, Tongjuan Wang: Research on Risk Evaluation in Supply Chain Based on GreyRelational Method. Journal of computers,3(10):28-35(2008)
[245] Huang Sunjen, Chiu Nanhsing, Chen Liwei. Integration of the grey relational analysis withgenetic algorithm for software effort estimation. European Journal of Operational Research188(2008)898-909.
[246] Ahn H, Kim K J. A Case-based Reasoning System with the Two dimensional ReductionTechnique for Customer Classification[J]. Expert Systems with Applications,2007,32(4):1011-1019.
[247] Deng Ju-Long. Control problems of grey systems. Systems&Control Letters1(5)(1982)288-294.
[248] Peide Liu, Tongjuan Wang: Research on Risk Evaluation in Supply Chain Based on GreyRelational Method. Journal of computers3(10)(2008)28-35.
[249] C.P. Fung, Manufacturing process optimization for wear property of fiber–reinforcedpolybutylene terephthalate composites with grey relational analysis, Wear254(2003)298–306.
[250] Y. Lu, X. He, J.J. Du. Malfunction case retrieval algorithm based on Grey System Theory.Computer Engineering of China,34(9):28-32(in Chinese)
[251] Victor R.L. Shen, Yu-Fang Chung, Tzer-Shyong Chen. A novel application of grey systemtheory to information security (Part I). Computer Standards&Interfaces, Volume31, Issue2,February2009, Pages277-281
[252] Erdal Kayacan, Baris Ulutas, Okyay Kaynak. Grey system theory-based models in timeseries prediction. Expert Systems with Applications, Volume37, Issue2, March2010, Pages1784-1789
[253]路杨,何欣,杜娟娟.基于灰色理论的故障案例检索算法[J].计算机工程,2008,34(9):28-29.
[254] Tsung-Ying Tsai Kai-Wei Chang. Calvin Yu-Chian Chen. iScreen: world’s first cloud-computing web server for virtual screening and de novo drug design based on TCM database@Taiwan. J Comput Aided Mol Des (2011)25:525–531
[2] Campadelli P, Casiraghi E, Artioli D. A fully automated method for lung nodule detectionfrom postero-anterior chest radiograph. IEEE Trans Med imaging,2006,25:1588-1603.
[3] Chen H, Wang XH, Ma DQ. Neural network-based computer-aided diagnosis indistinguishing malignant from benign solitary pulmonary nodules by computer tomography.Chin Med J,2007,120:1211-1215.
[4] Janet Kolodner, Reconstructive Memory: A Computer Model, Cognitive Science7(1983):4.
[5] Michael Lebowitz, Memory-Based Parsing, Artificial Intelligence21(1983),363-404.
[6] B.Porter, R.Bareiss, Robert Holte.Concept Learning and Heuristie Classifieation In WeakTheory Domains. ArtifieialIntelligence,1990,45:229-263.
[7] A.Aamodt, E.Plaza.Case-basedR easoning:Foundational Issue, Methodological Variation, andSystem Approaehes. Artificial Intelligenence Communication. IOS Press,1994,7(1):39-59.
[8] I.Gilboa, D.Sehaeidler. Case-based Decision Theory.Quarterly Journal of Economies,1995,110:605-639.
[9] F.Gebhardt, A.Vob, W.Grather, et al.Reasoning with complex Cases.Kluwer AcademicPublishers,1997.
[10] L. Maeedo, A. Cardoso. Nested Graph-Strueture Representations for Cases[A]. Europeanworkshop on Case-Based Reasoning’98[C].1998,1-11.
[11] Andres F.M. Rodriguez, Sunil Vadera.PEBM: A Probabillstic Exemplar Based model[A].International Joint Conference on Artifieial Intelligence [C],1999,242-247.
[12] Chiu, C. A case-based customer classification approach for direct marketing. ExpertSystemswith Applications2002,22:163-168.
[13]林闯.基于案例推理系统的Petri网模型.计算机学报,1994,计算机学报,17(A00):77-81.
[14] Bill Mark,"Case-Based Reasoning for Autoclave Management," Proceedings of theCase-Based Reasoning Workshop (1989).
[15] Trung Nguyen, Mary Czerwinski, and Dan Lee,"COMPAQ QuickSource: Providing theConsumer with the Power of Artificial Intelligence," in Proceedings of the Fifth AnnualConference on Innovative Applications of Artificial Intelligence (Washington, DC: AAAI Press,1993),142-151.
[16] Fenggang LI, Zhiwei NI.Case-Based Reasoning Based on Tabu Seareh[A].Proc.3rdIntl.Conf.on Maehine Learning and Cyberneties[C].Shanghai, August2004,2167-2171.
[17] Zhijei Ni, ShanLinYang, Yun Yang, Fenggang Li. Case-based Reasoning Framework. Basedon DataMining Teehnique[A].Proe.3th Intl.Conf.on Maehine Learning and Cyberneties[C].Shanghai, August2004,2511-2514.
[18] Zhiwei Ni, YuLiu, Fenggang Li. Case-based maintenanee based on outlier datamining[A].Proe.4th Intl.Conf.on Machine Learning and Cyberneties[C], Guangzhou, August,2005.
[19]梁昌勇,顾东晓,李兴国,杨善林.面向不确定多属性决策问题的范例检索算法研究[J].中国管理科学,2009,17(1):131-137.
[20]顾东晓,李兴国,梁昌勇,李锋刚.案例检索及权重优化方法研究及应用[J].系统工程学报,2009,24(6):764-768.
[21]于跃海,郑瑞强.基于案例推理的ICU应急诊断系统.系统工程理论与实践,2002,22(3):137-142.
[22]基于案例和模糊推理的农业虫害专家系统研究.计算机工程与设计.2007,28(22):5570-5572.
[23]杨健,马小兰.基于案例推理的中医诊疗专家系统.计算机工程,2008,34(21):178-180.
[24]陈晓红.决策支持系统理论和应用.北京：清华大学出版社，2000
[25] Helmush F. Orthner：http://homepage.uab.edu/horthner/scamc.htm
[26]崔雷,侯跃芳,张晗.论医疗决策支持系统[J].医学情报工作,2003,(1):28-40.
[27]赵起超.论医疗决策支持系统[J].中国医院管理,2001,21(244):40-41.
[28]王津涛,姚磊,姜恩海.基于SPSS的医疗统计决策支持系统设计与实现[J].计算机工程与设计,28（7）：1713-1715.
[29] J. Lieber and B. Bresson. Case-Based Reasoning for Breast Cancer Treatment DecisionHelping. In E. Blanzieri and L. Portinale, editors, Advances in Case-Based Reasoning-Proceedings of the fifth European Workshop on Case-Based Reasoning (EWCBR-2k), LNAI,1898, pages173–185. Springer,2000.
[30]刘兴华,蔡从中,袁前飞,等.基于支持向量机的乳腺癌辅助诊断.重庆大学学报,2007,30(6):140-144.
[31]范逢曦,张海,卢轶郎等.急性心肌梗塞急性期预后专家系统的研究[J].中国生物医学工程学报,1992,11(1):9-16.
[32]刘自伟.常见内科疾病中医诊疗专家辅助系统的设计及其实现[J].计算机时代,1994,(1):1-6.
[33]林东,邵军力.医学诊疗领域通用专家系统设计与实现[J].自动化学报,1995,21(3):380-382.
[34]花蕾等.基于知识的肺癌早期细胞诊断系统[J].计算机应用研究,2000,17(2):90-92.
[35]赵卫东,盛昭瀚,杜雪寒.基于神经网络的案例推理医疗诊断[J].东南大学学报,2000,30(3):46-50.
[36]徐宁,王宽全,张大鹏.基于神经网络算机应用研究,2001,18(2):4-6.
[37]王晶,卫金茂,由军平.支持向量机及其在癌症诊断中的应用[J].计算机工程与应用,2005,（36）：220-222.
[38]李凡,蔡立晶,田应忠.基于Vague集的医疗诊断系统.华中科技大学学报(自然科学版),2002,30(10):47-49.
[39]赵卫东,盛昭瀚.基于形象思维的医疗诊断系统研究[J].系统工程理论与实践,2000,20(10):108-113.
[40]邓锋,符云清,宋锦璘,等. OCDES：口腔癌医疗诊断专家系统设计与实现,计算机科学,2004；31(5):156-158.
[41]老年痴呆症诊断临床决策支持系统设计与评估[J].中国生物医学工程学报,2009,28(6)：872-877.
[42]张华,王崇骏,叶玉坤,陈世福. SARSES:SARS医疗辅助诊断专家系统的设计和实现[J];计算机工程与应用;2004,(18):217-220.
[43]马英豪李沪建徐勇勇孙金杰.军队卫生辅助决策信息系统研发与应用[J].解放军医院管理杂志,2009,16(8):748-750.
[44]李锋刚.基于案例推理的优化型智能决策技术的研究[D],合肥工业大学博士学位论文,2007.
[45] Acorn, T., and Walden, S., SMART: Support management automated reasoning technologyfor Compaq customer service. In Proceedings of the Tenth National Conference on ArtificialIntelligence. MIT Press.(1992).
[46] Hinkle, D., and Toomey, C. N., CLAVIER: Applying case-based reasoning on to compositepart fabrication. Proceeding of the Sixth Innovative Application of AI Conference, Seattle, WA,AAAI Press,(1994). pp.55-62.
[47] William M. Bain Judge: a case-based reasoning system Machine learning. Published inBook: a guide to current research, Kluwer Academic Publishers Norwell, MA, USA1986ISBN:0-89838-214-9.
[48] Cheetham, W., Tenth Anniversary of Plastics Color Matching, Artificial IntelligenceMagazine, Volume26, No.3,(2005). pp51–61.
[49] Callan,James P. CABOT: An Adaptive Approach to Case-Based Search. IJCAI'1991.pp.803-809
[50] Althoff, Klaus-Dieter, Ralph Bergmann, and L. Karl Branting, eds. Case-Based ReasoningResearch and Development: Proceedings of the Third International Conference on Case-BasedReasoning. Berlin: Springer Verlag,1999.
[51] Wittgentstein, L.(1953). Philosophical Investigations. Blackwell.
[52] Aamodt, A.&Plaza, E.(1994). Case-Based Reasoning: Foundational Issues,Methodological Variations, and System Approaches. AI Communications,7(i): pp39-59.
[53] Janet Kolodner, Reconstructive Memory: A Computer Model, Cognitive Science7(1983):4.
[54] Michael Lebowitz,"Memory-Based Parsing," Artificial Intelligence21(1983),363-404.
[55] Kolodner, J. L.(1983b). Reconstructive Memory: A Computer Model. Cognitive Science,7(iv): pp.281-28.
[56] Simpson, R. L.(1985). A Computer Model of Case-Based Reasoning in Problem Solving:An Investigation in the Domain of Dispute Mediation. Technical Report GIT-ICS-85/18, GeorgiaInstitute of Technology, School of Information and Computer Science, Atlanta,US.
[57] Hammond, K.J.(1986). CHEF: A Model of Case-Based Planning. In Proc. AmericanAssociation for Artificial Intelligence, AAAI-86, August1986. Philadelphia, PA, US.
[58] Sycara, E. P.(1987). Resolving adversial conflicts: An approach to Integrating Case-Basedand Analytic Methods. Technical Report GIT-ICS-87/26, Georgia Institute of Technology, Schoolof Information and Computer Science, Atlanta GA.
[59] Koton. P.(1989). Using experience in learning and problem solving. Massachusetts Instituteof Technology, Laboratory of Computer Science, Ph.D. Thesis MIT/LCS/TR-441.
[60] Hinrichs, T.R.(1992). Problem solving in open worlds. Lawrence Erlbaum Associates.
[61] Porter, B.W.&Bareiss, E.R.(1986). PROTOS: An experiment in knowledge acquisition forheuristic classification tasks. In Proceedings of the First International Meeting on Advnces inLearning (IMAL), Les Arcs, France, pp.159-74.
[62] Bareiss, E. R.,(1988). PROTOS: A Unified Approach to Concept Representation,Classification, and learning. Ph.D. thesis, Department. of Computer Science, University ofTexas.
[63] Branting, K.(1991). Exploiting the complementarity of rules and precedents withreciprocity and fairness. In, Proceedings of the Case-Bases Reasoning Workshop1991,Washington, DC, May1991. Sponsored by DARPA. Morgan Kaufmann, pp.39-50.
[64] Ashley, K.D.(1988). Arguing by Analogy in Law: A Case-Based Model. In D.H. Helman(Ed.), Analogical Reasoning: Perspectives of Artificial Intelligence, Cognitive Science, andPhilosophy. D. Reidel.
[65] Reisbeck, C.K,&Schank, R.C.(1989). Inside Case-Based Reasoning. Lawrence ErlbaumAssociates, Hillsdale, NJ, US.
[66] Sharma, S.&Sleeman, D.(1988). REFINER: A Case-Based Differential Diagnosis Aide forKnowledge Acquisition and Knowledge Refinement. In, EWSL88; Proc. European WorkingSession on Learning: pp201-10.
[67] Keane, M.,(1988). Where's the Beef? The absence of pragmatic factors in theories ofanalogy. In, ECAI-88: pp.327-32.
[68] Watson, I.D.,&Abdullah, S.(1994). Developing Case-Based Reasoning Systems: A CaseStudy in Diagnosing Building Defects. In, Proc. IEE Colloquium on Case-Based Reasoning:Prospects for Applications, Digest No:1994/057, pp.1/1-1/3.
[69] Yang, S.,&Robertson, D.(1994). A case-based reasoning system for regulatory information.In, Proc. IEE Colloquium on Case-Based Reasoning: Prospects for Applications, Digest No:1994/057, pp.3/1-3/3.
[70] Moore, C.J., Lehane, M.S.&Proce, C.J.(1994). Case-Based Reasoning for DecisionSupport in Engineering Design. In, Proc. IEE Colloquium on Case-Based Reasoning: Prospectsfor Applications, Digest No:1994/057, pp.4/1-4/4.
[71] Althoff, K.D.(1989). Knowledge acquisition in the domain of CBC machine centres: theMOLTKE approach. In, EKAW-89, Third European Workshop on Knowledge-Based Systems,Boos, J., Gaines, B.&Ganascia, J.G.(eds.), pp.180-95. Paris, July1989.
[72] Richter, A.M.&Weiss, S.(1991). Similarity, uncertainty and case-based reasoning inPATDEX. In, Automated reasoning, essays in honour of Woody Bledsoe. Kluwer R.S. Boyer(ed.): pp249-265.
[73] Oxman, R.E.,(1993a). PRECEDENTS: Memory structure in design case libraries. InCAAD Futures93, Elsevier Science Publishers.
[74] Oxman, R.E.,(1993b). Case-based design support: Supporting architectural compositionthrough precedent libraries. Journal of Architectural Planning Research.
[75] Venkatamaran, S., Krishnan, R.&Rao, K.K.(1993). A rule-case based system for imageanalysis. In, Proc.1st. European Workshop on Case-Based Reasoning, Posters&Presentations,2:pp.410-15.
[76] Isabelle Bichindaritz，Cindy Marling. Case-based reasoning in the health sciences: What'snext?，Artificial Intelligence in Medicine2006;36(2):127-135.
[77] Stefania Montani，Exploring new roles for case-based reasoning in heterogeneous AIsystems for medical decision support，Applied Intelligence2008;28(3):275-285.
[78] Janet Kolodner, Case-based reasoning, Morgan Kaufmann Publishers Inc., San Francisco,CA,1993.
[79] Monica H. Ou, Geoff A.W. West, Mihai Lazarescu, Chris Clay. Dynamic knowledgevalidation and verification for CBR teledermatology system, Artificial Intelligence in Medicine,Volume39, Issue1,2007, Pages79-96.
[80] Hui Li, Jie Sun. Hybridizing principles of the Electre method with case-based reasoning fordata mining: Electre-CBR-I and Electre-CBR-II, European Journal of Operational Research,Volume197, Issue1,2009, Pages214-224.
[81] Monique Frize, Robin Walker. Clinical decision-support systems for intensive care unitsusing case-based reasoning, Medical Engineering&Physics, Volume22, Issue9,2000, Pages671-677
[82] Marion C.J. Biermans, Dinny H. de Bakker, Robert A. Verheij, Jan V. Gravestein, Michiel W.van der Linden, Pieter F. de Vries Robbé. Development of a case-based system for groupingdiagnoses in general practice, International Journal of Medical Informatics, Volume77, Issue7,2008, Pages431-439.
[83] Chien-Chang Hsu, Cheng-Seen Ho. A new hybrid case-based architecture for medicaldiagnosis, Information Sciences, Volume166, Issues1-4,2004, Pages231-247
[84] Syed Sibte Raza Abidi, Selvakumar Manickam. Leveraging XML-based electronic medicalrecords to extract experiential clinical knowledge: An automated approach to generate cases formedical case-based reasoning systems, International Journal of Medical Informatics, Volume68,Issues1-3,2002, Pages187-203.
[85] Rainer Schmidt, Lothar Gierl. A prognostic model for temporal courses that combinestemporal abstraction and case-based reasoning, International Journal of Medical Informatics,Volume74, Issues2-4,2005, Pages307-315.
[86] Amjad Waheed, Hojjat Adeli. Case-based reasoning in steel bridge engineering.Knowledge-Based Systems, Volume18, Issue1,2005, Pages37-46.
[87] Chi-man Vong, Pak-kin Won. Case-based adaptation for automotive engine electroniccontrol unit calibration. Expert Systems with Applications, Volume37, Issue4,2010, Pages3184-3194.
[88] Kefeng Hua, Boi Fairings, Ian Smith. CADRE: case-based geometric design. ArtificialIntelligence in Engineering, Volume10, Issue2,1996, Pages171-183
[89] Atish P. Sinha and Jerrold H. May.Providing Design Assistance: A Case-Based Approach.Information Systems Research,1996;7:363-387.
[90] Kyung-shik Shin, Ingoo Han. A case-based approach using inductive indexing for corporatebond rating. Decision Support Systems, Volume32, Issue1,2001, Pages41-52
[91] Chaochang Chiu. A case-based customer classification approach for direct marketing.Expert Systems with Applications, Volume22, Issue2, February2002, Pages163-168
[92] Kamalendu, Owen. A decision support system for business acquisitions. Decision SupportSystems, Volume27, Issue4,2000, Pages411-429.
[93] Günter Schmidt. Case-based reasoning for production scheduling. International Journal ofProduction Economics, Volumes56-57,1998, Pages537-546.
[94] Y.F. Li, M. Xie, T.N. Goh. A study of mutual information based feature selection for casebased reasoning in software cost estimation.Expert Systems with Applications, Volume36, Issue3, Part2,2009, Pages5921-5931.
[95] Mukhopadhyay, T., Vicinanaza, S.S, Prieutula, M.J.(1992). Examining the feasibility of acase-based reasoning model for software effort estimation. MIS Quarterly,16(2), pp.155-72.
[96] Robert D. Austin and Lee Devin. Redesigning Case Retrieval to Reduce InformationAcquisition Costs. Information Systems Research,1997;8:51-68.
[97] Robert D. Austin and Lee Devin. Research Commentary—Weighing the Benefits and Costsof Flexibility in Making Software: Toward a Contingency Theory of the Determinants ofDevelopment Process Design. Information Systems Research,2009;20:462–477
[98] Abdus Salam Khan, Achim Hoffmann. Building a case-based diet recommendation systemwithout a knowledge engineer, Artificial Intelligence in Medicine, Volume27, Issue2,2003,Pages155-179.
[99] Raquel Ros, Josep Lluís Arcos, Ramon Lopez de Mantaras, Manuela Veloso. A case-basedapproach for coordinated action selection in robot soccer. Artificial Intelligence, Volume173,Issues9-10,2009, Pages1014-1039.
[100] Ting-Peng Liang. Analogical reasoning and case-based learning in model managementsystems. Decision Support Systems, Volume10, Issue2,1993, Pages137-160
[101] Stephan Grolimund, Jean-Gabriel Ganascia. Driving Tabu Search with case-basedreasoning, European Journal of Operational Research, Volume103, Issue2,1997, Pages326-338
[102] Hamid R. Nemati, David M. Steiger, Lakshmi S. Iyer, Richard T. Herschel. Knowledgewarehouse: an architectural integration of knowledge management, decision support, artificialintelligence and data warehousing, Decision Support Systems, Volume33, Issue2,2002, Pages143-161.
[103] Yong Sik Chang, Jae Kyu Lee. Case-based modification for optimization agents:AGENT-OPT, Decision Support Systems, Volume36, Issue4,2004, Pages355-370.
[104] Vasyl Golosnoy, Yarema Okhrin. General uncertainty in portfolio selection: A case-baseddecision approach. Journal of Economic Behavior&Organization, Volume67, Issues3-4,2008,Pages718-734.
[105] Heng Li. Case-based reasoning for intelligent support of construction negotiation.Information&Management, Volume30, Issue5,1996, Pages231-238.
[106] Lyle Brenner, Dale Griffin, Derek J. Koehler. Modeling patterns of probability calibrationwith random support theory: Diagnosing case-based judgment. Organizational Behavior andHuman Decision Processes, Volume97, Issue1,2005, Pages64-81.
[107] Matthias Blonski. Social learning with case-based decisions, Journal of EconomicBehavior&Organization, Volume38, Issue1,1999, Pages59-77.
[108] Elisabet Golobardes, Xavier Llorà, Maria Salamó, Joan Martí. Computer aided diagnosiswith case-based reasoning and genetic algorithms. Knowledge-Based Systems, Volume15,Issues1-2,2002, Pages45-52.
[109] E.B Reategui, J.A Campbell, B.F Leao. Combining a neural network with case-basedreasoning in a diagnostic system, Artificial Intelligence in Medicine, Volume9, Issue1,1997,Pages5-27.
[110] Zoe Y. Zhuang, Leonid Churilov, Frada Burstein, Ken Sikaris. Combining data mining andcase-based reasoning for intelligent decision support for pathology ordering by generalpractitioners, European Journal of Operational Research, Volume195, Issue3,2009, Pages662-675.
[111] Javier Bajo, Juan F. de Paz, Yanira de Paz, Juan M. Corchado, Integrating case-basedplanning and RPTW neural networks to construct an intelligent environment for health care,Expert Systems with Applications: An International Journal, v.36n.3, p.5844-5858, April,2009
[112] Oh Byung Kwon, Norman Sadeh. An expert system with case-based reasoning for databaseschema design. Decision Support Systems, Volume18, Issue1,1996, Pages83-95
[113] Pei-Chann Chang, Chien-Yuan Lai, K. Robert Lai. A hybrid system by evolving case-basedreasoning with genetic algorithm in wholesaler's returning book forecasting. Decision SupportSystems, Volume42, Issue3,2006, Pages1715-1729.
[114] Jocelyn San Pedro, Frada Burstein, Alan Sharp. A case-based fuzzy multicriteria decisionsupport model for tropical cyclone forecasting. European Journal of Operational Research,Volume160, Issue2,2005, Pages308-324.
[115] Sheng-Tun Li, Hei-Fong Ho. Predicting financial activity with evolutionary fuzzycase-based reasoning. Expert Systems with Applications, Volume36, Issue1,2009, Pages411-422
[116] F. Faez, S.H. Ghodsypour, C. O’Brien. Vendor selection and order allocation using anintegrated fuzzy case-based reasoning and mathematical programming model, InternationalJournal of Production Economics, Volume121, Issue2,2009, Pages395-408
[117] Oh Byung Kwon, Norman Sadeh. Applying case-based reasoning and multi-agentintelligent system to context-aware comparative shopping, Decision Support Systems, Volume37, Issue2,2004, Pages199-213.
[118] Aasia Khanum, Muid Mufti, M. Younus Javed, M. Zubair Shafiq. Fuzzy case-basedreasoning for facial expression recognition. Fuzzy Sets and Systems, Volume160, Issue2,162009, Pages231-250.
[119] K.M. Saridakis, A.J. Dentsoras. Case-DeSC: A system for case-based design with softcomputing techniques, Expert Systems with Applications, Volume32, Issue2,2007, Pages641-657.
[120] A. H. Mohamed, F. A. Mohamed, A. M. Nassar, M. H. El-Fouly. Case-functional-baseddiagnostic system (CFDS), Engineering Applications of Artificial Intelligence, Volume15, Issue5,2002, Pages501-509.
[121] Malrey Lee. A study of an automatic learning model of adaptation knowledge for case basereasoning, Information Sciences, Volume155, Issues1-2,2003, Pages61-78
[122]史忠植.高级人工智能[M].北京:科学出版社,1998.
[123]刘芳,姚莉,王长缨.基于语义Web的案例表示和CBR系统结构研究[J].计算机应用,2004,24(1):17-19.
[124]张月雷,左洪福.基于本体的案例表示和CBR系统结构研究[J].山东理工大学学报:自然科学版,2006,20(4):48-51.
[125]丁剑飞,何玉林,李成武.基于本体的分布式CBR设计系统[J].计算机工程,2007,33(21):183-185.
[126] Aamodt A. Knowledge intensive case-based reasoning in CREEK[C].Proc of the7thEuropean Conference on Case-based Reasoning. Berlin: Spring,2004:291-305.
[127] Diaz-Agudo B, Gonzalez-Calero P A. Knowledge intensive CBR through ontologies[J].Expert Update,2003,6(1):44-54.
[128] Salton. Introduction to Modern Information Retrieval [J]. McGraw-Hill Company,1983,120-130.
[129]陈朝阳,张代胜,任佩红. CBR诊断系统实例获取的合成相似性度量方法[J].机械工程学报,2004,40(5):48-51.
[130] Vong C M,Leung T P,Wong P K.Case-based reasoning and adaptation in hydraulicproduction machine design[J].Engineering Applications of Artificial Intelligence,2002,15:567-585.
[131]汤廷孝,刘勇等. CBR系统中的实例修改研究[J].机械科学与技术,2006,25(4):390-393
[132]Lee M. A study of automatic learning model of adaptation knowledge for case-basedreasoning[J].Information Sciences,2003,155:61-78.
[133] Smyth B,Keane M T.Using adaptation knowledge to retrieve and adapt designcases[J].Knowledge Based System,1996,9:127-135.
[134] Virkki Hatakka T,et al.Adaptation phase in case-based reasoning system for processequipment selection[J].Computers Chemical Engineering,1997,21:643-648.
[135]张斌,高全杰,应保胜.实例推理和规则推理在实例修改中的应用[J].计算机工程,2005,31(13):156-158.
[136] S.L. Chan, W.H. Ip. A dynamic decision support system to predict the value of customerfor new product development. Decision Support Systems, In Press, Corrected Proof, Availableonline2011.
[137] Noel Bryson, Ayodele Mobolurin. An approach to using the analytic hierarchy process forsolving multiple criteria decision making problems. European Journal of Operational Research,Volume76, Issue3,1994, Pages440-454.
[138]胡中辉,李烨,蔡云泽,黄金杰.基于属性约简及支持向量机的医疗诊断决策研究[J].计算机工程与应用,2005,(13):183-185.
[139] Slezak, Dominik,Ziarko,Wojciech. Attibute reduction in Bayesian version of variableprecision rough set model. Electronic Notes in Theoretical Computer Science,2003,82(4):1-11.
[140] Jensen, Richard, Shen, Qiang. Fuzzy-rough attributes reduction with application to webcategorization. Fuzzy Sets and Systems,2004,141(3):469-485.
[141] Kemal Polat, Salih Güne. A hybrid medical decision making system based on principlescomponent analysis, k-NN based weighted pre-processing and adaptive neuro-fuzzy inferencesystem. Digital Signal Processing, Volume16, Issue6,2006, Pages913-921
[142] Engin Avci, Ibrahim Turkoglu. An intelligent diagnosis system based on principlecomponent analysis and ANFIS for the heart valve diseases. Expert Systems with Applications,Volume36, Issue2, Part2,2009, Pages2873-2878
[143] Greene，Derek，Freyne，Jill，et al.(2008). An Analysis of Research Themes in the CBRConference Literature. In Althoff，K.-D.，Bergmann，R.，Minor，M.，Hanft，A.(Eds.)，Advancesin Case-Based Reasoning-Proceedings of the9th European Workshop on Case-Based Reasoning(ECCBR2008)，Trier，Germany，September1-4，18-43. Springer.
[144] Zhang，Lu，Coenen，Frans and Leng，Paul.(2002). Formalising optimal feature weightsetting in case based diagnosis as linear programming problems，Knowledge-Based Systems15(7):391-398.
[145] Renauda, J., Levratb, E. and Fonteixc, C.(2008). Weights determination of OWA operatorsby parametric identification, Mathematics and Computers in Simulation77:499–511.
[146] Schaaf, J.W., Fish and shrink.(1996). A next step towards efficient case retrieval in largescaled case bases, in: Advances in Case-based Reasoning: Third European Workshop, Lausanne,Switzerland,362–377.
[147] Saridakis, K.M., Dentsoras, A.J..(2007). Case-DeSC: A system for case-based design withsoft computing techniques, Expert Systems with Applications32(2):641-657.
[148] Zou, Zhi-hong, Yun, Yi and Sun, Jing-nan.(2006). Entropy method for determination ofweight of evaluating indicators in fuzzy synthetic evaluation for water quality assessment,Journal of Environmental Sciences18(5):1020-1023.
[149] Reategui, E.B, Campbell, J.A, Leao, B.F.(1997). Combining a neural network withcase-based reasoning in a diagnostic system, Artificial Intelligence in Medicine9(1):5–27.
[150] Schmidt R, Gierl L.(2001). Case-based reasoning for antibiotics therapy advice: aninvestigation of retrieval algorithms and prototypes. Art Intelligence in Medicine23(2):171-186.
[151] Singh, M., Mandal, M.K., Basu, A.(2005). Gaussian and Laplacian of Gaussian weightingfunctions for robust feature based tracking, Pattern Recognition Letters26(13):1995–2005.
[152] Tan, Tuan Zea and Quek, Chai.(2008). Ovarian cancer diagnosis with complementarylearning fuzzy neural network, Artificial Intelligence in Medicine43:207-222.
[153] West, David, Mangiameli, Paul, Rampal, Rohit, West, Vivian.(2005). Ensemble strategiesfor a medical diagnostic decision support system: A breast cancer diagnosis application.European Journal of Operational Research162(2):532-551.
[154] E.P. Xing, M.I. Jordan, R.M. Karp, Feature selection for high-dimensional genomicmicroarray data, in: Proc. of the18th International Conference on Machine Learning, pp.601–608,(2001).
[155] J. Friedman, T. Hastie, R. Tibshirani, The elements of statistical learning. Springer,(2001).
[156] Hosmer, D. W., and Lemeshow, S., Applied logistic regression,2nd edition. Wiley, NewYork,2000.
[157]Mammogram Interpretation: Categories and the ACR/BI-RADS,http://www.imaginis.com/breasthealth/acrbi.asp,(2007).
[158] M. Elter, R. Schulz-Wendtland, T. Wittenberg, Mammographic Mass Data Set,http://archive.ics.uci.edu/ml/datasets/Mammographic+Mass,2007
[159] N. Marchettini, R.M. Pulselli, F. Rossi, E. Tiezzi. Entropy, Encyclopedia of Ecology,2008,Pages1297-1305.
[160] Balian, Roger.(2003). Entropy–Protean Concept. Poincaré Seminar2:119-45.
[161] Jaynes E T.(1957). Information theory and statistical mechanics. Physical Review106(4):620-630.
[162] Zhang, Lu, Coenen, Frans and Leng, Paul.(2002). Formalising optimal feature weightsetting in case based diagnosis as linear programming problems, Knowledge-Based Systems15(7):391-398.
[163] Zhuang, Zoe Y., Churilov, Leonid, et al.(2009). Combining data mining and case-basedreasoning for intelligent decision support for pathology ordering by general practitioners,European Journal of Operational Research195(3):662-675.
[164] Tuan Zea Tan, Chai Quek, Geok See Ng, Khalil Razvi. Ovarian cancer diagnosis withcomplementary learning fuzzy neural network, Artificial Intelligence in Medicine,(2008)43,207-222
[165] Isabelle Bichindaritz, Cindy Marling, Case-based reasoning in the health sciences: What'snext?, Artificial Intelligence in Medicine,2006,36(2):127-135.
[166] Stefania Montani, Exploring new roles for case-based reasoning in heterogeneous AIsystems for medical decision support, Applied Intelligence,2008,28(3):275-285.
[167] Markus Nilsson, Peter Funk, Erik M. G. Olsson, Bo von Schéele, Ning Xiong, Clinicaldecision-support for diagnosing stress-related disorders by applying psychophysiologicalmedical knowledge to an instance-based learning system, Artificial Intelligence in Medicine,2006,36(2):159-176.
[168] Marion C.J. Biermans, Dinny H. de Bakker, Robert A. Verheij, Jan V. Gravestein, MichielW. van der Linden, Pieter F. de Vries Robbé. Development of a case-based system for groupingdiagnoses in general practice, International Journal of Medical Informatics2008;77(7):431-439.
[169] Chien-Chang Hsu, Cheng-Seen Ho. A new hybrid case-based architecture for medicaldiagnosis, Information Sciences2004;166(1-4):231-247
[170] A.O. Bilska-Wolak and C.E. Floyd Jr., Development and evaluation of a case-basedreasoning classifier for prediction of breast biopsy outcome with bi-radstm lexicon, MedicalPhysics29(9)(2002),2090-2100.
[171] Juan M. Corchado, Javier Bajo, Yanira de Paz, Dante I. Tapia, Intelligent environmentfor monitoring Alzheimer patients, agent technology for health care, Decision Support Systems,v.44n.2, p.382-396,2008
[172] Cindy Marling, Peter Whitehouse, Case-Based Reasoning in the Care of Alzheimer'sDisease Patients, Proceedings of the4th International Conference on Case-Based Reasoning:Case-Based Reasoning Research and Development, p.702-715,2001
[173] Aquin M, Lieber J, Napoli A (2006) Adaptation knowledge acquisition: a case study forcase-based decision support in oncology. In: Bichindaritz I, Marling C (eds) Special issue onCBR in the health sciences. Comput Intell22(3-4):161-176
[174] Schmidt R, Gierl L, Case-based reasoning for antibiotics therapy advice: an investigationof retrieval algorithms and prototypes. Art Intelligence in Medicine2001;23(2):171-186
[175] Herrera F, Martinez L. An approach for combining numerical and linguistic informationbased on the2-tuple fuzzy linguistic representation model in decision making [J]. InternationalJournal of Uncertainty, Fuzziness and Knowledge-Based Systems,2000,8:539-562.
[176] Li D F, Yang J B. Fuzzy linear programming technique for multi-attribute group decisionmaking in fuzzy Environments [J]. Information Sciences,2004,158:263-275.
[177] Xu Z S. Uncertain linguistic aggregation operators based approach to multiple attributegroup decision making under uncertain linguistic environment [J]. Information Sciences,2004,(168):171-184.
[178] Xu Z. Group decision making with triangular fuzzy linguistic variables[C]//IDEAL2007,LNCS,2007,4881:17-26.
[179] Liang X C, Chen S F. Multiple at tribute decision making metho d based o n trapezoidfuzzy linguistic var iables [J]. Journal of Southeast University (English Edition),2008,24(4):478-481.
[180] Héctor Nú ez, Miquel Sànchez-Marrè, Ulises Cortés, Joaquim Comas, Montse Martínez,Ignasi Rodríguez-Roda, Manel Poch. A comparative study on the use of similarity measures incase-based reasoning to improve the classification of environmental system situations,Environmental Modeling&Software, Volume19, Issue9, September2004, Pages809-819
[181] S. Oliveira, J.F.F. Ribeiro, S.C. Seok. A comparative study of similarity measures formanufacturing cell formation, Journal of Manufacturing Systems, Volume27, Issue1, January2008, Pages19-25
[182] B. De Baets and H. De Meyer, Transitivity-preserving fuzzification schemes forcardinality-based similarity measures, European J. Oper. Res.160(2005), pp.726–740
[183] C.S. Park, I. Han, A case-based reasoning with the feature weights derived by analytic255–264
[184] Feng Kong. Theory, Methods and Applications for Fuzzy Multiple Attribute DecisionMaking. Beijing: China Agricultural Science Press.2008.3
[184] Chen, C. T. Extensions of the TOPSIS for group decision-making under fuzzy environment.Fuzzy Sets and Systems,114(1)(2000),1-9.
[184] Jin Qi, Jie Hu, Ying-hong Peng, Wei-ming Wang, Zhenfei Zhang: A case retrieval methodcombined with similarity measurement and multi-criteria decision making for concurrent design.Expert Syst. Appl.36(7)(2009):10357-10366begin_of_the_skype_highlighting
[187] Ian H. Witten; Eibe Frank (2005)."Data Mining: Practical machine learning tools andtechniques,2nd Edition". Morgan Kaufmann, San Francisco.http://www.cs.waikato.ac.nz/~ml/weka/book.html. Retrieved2007-06-25.
[188] http://en.wikipedia.org/wiki/Sensitivity_and_specificity
[189]American Cancer Society. Breast Cancer Facts&Figures2009-2010(Web Site).http://www.cancer.org/downloads/STT/F861009_final%209-08-09.pdf. February18,2010
[190]American Cancer Society. Breast Cancer Facts&Figures2009-2010(Web Site).http://www.cancer.org/downloads/STT/500809web.pdf. February18,2010
[191]D. Max Parkin, Freddie Bray, J. Ferlay and Paola Pisani. Global Cancer Statistics,2002,CA Cancer J Clin2005;55:74-108
[192]P Hider and B Nicholas, The early detection and diagnosis of breast cancer: literaturereview–an update. New Zealand Health Technology Assessment Clearing House (NZHTA)Report (1999), p.2-32
[193] Yarnold, J. Early and Locally Advanced Breast Cancer: Diagnosis and Treatment NationalInstitute for Health and Clinical Excellence Guideline2009
[194] N. Houssami, S. Ciatto, F. Martinelli, R. Bonardi, and S. W. Duffy, Early detection ofsecond breast cancers improves prognosis in breast cancer survivors, Ann. Onc.,2009;20(9):1505-1510.
[195] Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and prognosis vialinear programming. Operations Research,1995,43(4), pages570-577
[196] T. Ayer, J. Chhatwal, O. Alagoz, C. E. Kahn Jr, R. W. Woods, and E. S. Burnside.Informatics in Radiology: Comparison of Logistic Regression and Artificial Neural NetworkModels in Breast Cancer Risk Estimation, Radio Graphics,2010;30(1):13-22.
[197]Hung et al., M. Hung, M. Shanker and M. Hu, Estimating breast cancer risks using neuralnetworks, Journal of the Operational Research Society53(2002), pp.222–231
[198] J.L. Castro, M. Navarro, J.M. Sánchez, J.M. Zurita. Loss and gain functions for CBRretrieval, Information Sciences, Volume179, Issue11,2009, Pages1738-1750
[199] H. Li, J. Sun, Ranking-order case-based reasoning for financial distress prediction,Knowledge-Based Systems21(8)(2008)868–878.
[200] Hui Li, Jie Sun. Gaussian case-based reasoning for business failure prediction withempirical data in China. Information Sciences, Volume179, Issues1-2,2009, Pages89-108
[201] David West, Paul Mangiameli, Rohit Rampal, Vivian West. Ensemble strategies for amedical diagnostic decision support system: A breast cancer diagnosis application. EuropeanJournal of Operational Research, Volume162, Issue2,2005, Pages532-551
[202]T. Ayer, J. Chhatwal, O. Alagoz, C. E. Kahn Jr, R. W. Woods, and E. S. Burnside.Informatics in Radiology: Comparison of Logistic Regression and Artificial Neural NetworkModels in Breast Cancer Risk Estimation, Radio Graphics2010;30(1):13-22.
[203] Hung et al., M. Hung, M. Shanker and M. Hu. Estimating breast cancer risks using neuralnetworks, Journal of the Operational Research Society2002;53:222–231.
[204] J. Lieber and B. Bresson. Case-Based Reasoning for Breast Cancer Treatment DecisionHelping. In E. Blanzieri and L. Portinale, editors, Advances in Case-Based Reasoning-Proceedings of the fifth European Workshop on Case-Based Reasoning (EWCBR-2k), LNAI,1898, pages173–185. Springer,2000.
[205] C. E. Floyd, Jr., J. Y. Lo, and G. D. Tourassi,"Case-based reasoning computer algorithmthat uses mammographic findings for breast biopsy decisions," Am. J. Roentgenol.175,1347–1352(2000)
[206] A.O. Bilska and C. E. Floyd, Jr.,"Investigating different similarity measures for acase-based reasoning classifier to predict breast cancer," Proc. SPIE Med. Imaging4322,1862–1866(2001)
[207] K.S. Shin, I. Han. Case-based reasoning supported by genetic algorithms for corporatebond rating, Expert Systems with Applications16(1999)85–95.
[208] K.J. Kim, I. Han. Maintaining case-based reasoning systems using a genetic algorithmsapproach, Expert Systems with Applications21(2001)139–145.
[209] C. Chiu. A case-based customer classification approach for direct marketing, ExpertSystems with Applications22(2002)163–168.
[210] Y. Fu, R. Shen. GA based CBR approach in Q&A system, Expert Systems withApplications26(2004)167–170.
[211] Gu DX, Liang CY, Li XG, et al. Intelligent Technique for Knowledge Reuse of DentalMedical Records Based on Case-Based Reasoning. Journal of medical systems2010;34(2):213-222
[212]Brendan McCane, Michael Albert. Distance functions for categorical and mixed variables.Pattern Recognition Letters,2008;29(7):986-993
[213] C. Stanfill and D.L. Waltz,“Toward Memory-Based Reasoning,”Comm. ACM, vol.29, pp.1213-1228,1986.
[214]Gower, J.C.,1971. A general coefficient of similarity and some of its properties. Biometrics27,857–874.
[215]D. Wilson and T. Martinez,“Improved Heterogeneous Distance Functions,” J. ArtificialIntelligence Research, vol.6, pp.1-34,1997.
[216]Derek Greene, Jill Freyne, Barry Smyth, Padraig Cunningham: An Analysis of ResearchThemes in the CBR Conference Literature. ECCBR2008:18-43
[217]J. Renauda, E. Levratb, C. Fonteixc, Weights determination of OWA operators byparametric identification, Mathematics and Computers in Simulation2008;77:499–511
[218] Lu Zhang, Frans Coenen, Paul Leng. Formalising optimal feature weight setting incase-based diagnosis as linear programming problems, Knowledge-Based Systems2002;15(7):391-398.
[219]C.S. Park, I. Han, A case-based reasoning with the feature weights derived by analytichierarchy process for bankruptcy prediction, Expert Systems with Applications2002;23(3):255–264
[220]S.Z. Dogan, D. Arditi, H.M. Gnaydin, Using decision trees for determining attributeweights in a case-based model of early cost prediction, Journal of Construction Engineering andManagement2008;134(2):146–152
[221]M. V. Fidelis, H. S. Lopes, and A. A. Freitas. Discovering Comprehensible ClassificationRules with a Genetic Algorithm. In Proc. Of the2000Congress on Evolutionary Computation,1,pp.805-810,2000.
[222] Goldberg, D.E. Genetic Algorithms in Search, Optimization and Machine Learning.Reading: Addison-Wesley,1989.
[223]Cunningham, P. A taxonomy of similarity mechanisms for case-based reasoning. IEEETransactions on Knowledge and Data Engineering,2008;21(11):1532-1543
[224]D. West, P. Mangiameli, R. Rampal and V. West, Ensemble strategies for a medicaldiagnosis previous termdecisionnext term support system: a breast previous term cancer nextterm diagnosis application, Eur. J. Operat. Res.162(2005), pp.532–551
[225] Sangjae Lee. Using data envelopment analysis and decision trees for efficiency analysisand recommendation of B2C controls. Decision Support Systems, Volume49, Issue4,2010,Pages486-497
[226]RBF network L.M. Salchenberger, E.M. Cinar and N.A. Lash, Neural networks: a new toolfor predicting thrift failures, Decision Sciences23(4)(1992), pp.899–916
[227]RBF network Murat Karabatak, M. Cevdet Ince. An expert system for detection of breastcancer based on association rules and neural network. Expert Systems with Applications, Volume36, Issue2, Part2,2009, Pages3465-3469
[228] RBF network Randall S. Sexton, Robert E. Dorsey. Reliable classification using neuralnetworks: a genetic algorithm and back propagation comparison. Decision Support Systems,Volume30, Issue1,2000, Pages11-22
[229] Zhen Zhang, Hong Zhang, Robert C. Bast Jr. An Application of Artificial Neural Networksin Ovarian Cancer Early Detection, Proceedings of the IEEE-INNS-ENNS International JointConference on Neural Networks (IJCNN'00)-Volume4, p.4107, July24-27,2000
[230] RBF network Tuan Zea Tan, Chai Quek, Geok See Ng, Khalil Razvi, Ovarian cancerdiagnosis with complementary learning fuzzy neural network, Artificial Intelligence in Medicine,v.43n.3, p.207-222, July,2008
[231] RBF network Yuehui Chen, Yan Wang, Bo Yang: Evolving Hierarchical RBF NeuralNetworks for Breast Cancer Detection. ICONIP (3)2006:137-144
[232] Cruz-Ramirez N, Acosta-Mesa HG, Carrillo-Calvet H, et al. Discovering interobservervariability in the cytodiagnosis of breast cancer using decision trees and Bayesian networks.Applied Soft Computing,20099(4):1331-1342
[233] Naive bayes P. Antal, H. Verrelst, D. Timmerman, S. Van Huffel, B. de Moor, I. Vergote,Bayesian Networks in Ovarian Cancer Diagnosis: Potentials and Limitations, Proceedings of the13th IEEE Symposium on Computer-Based Medical Systems (CBMS'00), p.103, June23-24,2000
[234] Logistics Chun-Lang Chang, Ming-Yuan Hsu. The study that applies artificial intelligenceand logistic regression for assistance in differential diagnostic of pancreatic cancer. ExpertSystems with Applications, Volume36, Issue7,2009, Pages10663-10672
[235] I.H. Witten and E. Frank, Data mining: practical machine learning tools and techniques(second ed.), Morgan Kaufmann, San Francisco (2005).
[236]Bichindaritz and C. Marling. Case-based reasoning in the health sciences: What's next?Artificial Intelligence in Medicine,36(2):127–135,2006
[237]Klaus-Dieter Althoff, Ralph Bergmann, et al. Case-based reasoning for medical decisionsupport tasks: The Inreca approach. Artificial Intelligence in Medicine,1998,12(1):25-41
[238]Venkatesh, V., Speier, C.,&Morris, M. G.(2002). User acceptance enablers in individualdecision making about technology: Toward an integrated model. Decision Sciences,33(2),297-316.
[239]Wixom, B. H.,&Todd, P. A.(2005). A theoretical integration of user satisfaction andtechnology acceptance. Information Systems Research,16(1),85-102.
[240]Venkatesh, V.,&Morris, M. G.(2000). Why don't men ever stop to ask for directions?gender, social influence, and their role in technology acceptance and usage behavior. MISQuarterly,24(1),115-139.
[241]Agnar Aamodt and Enric Plaza. Case-Based Reasoning: Foundational Issues,Methodological Variations, and System Approaches. Artificial Intelligence Communications, IOSPress7(1)(1994)39-59.
[242]Ramon López de Mántaras, David McSherry, Derek Bridge, David Leake, Barry Smyth,Susan Craw, Boi Faltings, Mary Lou Maher, Michael Cox, Kenneth Forbus, Mark Keane, AgnarAamodt, and Ian Watson. Retrieval, reuse, revision, and retention in case-based reasoning.Knowledge Engineering Review. Cambridge University Press20(3)(2005)215-240.
[243] Bichindaritz, I., Marling, C., Case-based Reasoning in the Health Sciences: What’s Next?,Artificial Intelligence in Medicine, Special Issue on Case-based Reasoning in the HealthSciences, Bichindaritz, I.(Edt.)36(2)(2006)127-135.
[244] Peide Liu, Tongjuan Wang: Research on Risk Evaluation in Supply Chain Based on GreyRelational Method. Journal of computers,3(10):28-35(2008)
[245] Huang Sunjen, Chiu Nanhsing, Chen Liwei. Integration of the grey relational analysis withgenetic algorithm for software effort estimation. European Journal of Operational Research188(2008)898-909.
[246] Ahn H, Kim K J. A Case-based Reasoning System with the Two dimensional ReductionTechnique for Customer Classification[J]. Expert Systems with Applications,2007,32(4):1011-1019.
[247] Deng Ju-Long. Control problems of grey systems. Systems&Control Letters1(5)(1982)288-294.
[248] Peide Liu, Tongjuan Wang: Research on Risk Evaluation in Supply Chain Based on GreyRelational Method. Journal of computers3(10)(2008)28-35.
[249] C.P. Fung, Manufacturing process optimization for wear property of fiber–reinforcedpolybutylene terephthalate composites with grey relational analysis, Wear254(2003)298–306.
[250] Y. Lu, X. He, J.J. Du. Malfunction case retrieval algorithm based on Grey System Theory.Computer Engineering of China,34(9):28-32(in Chinese)
[251] Victor R.L. Shen, Yu-Fang Chung, Tzer-Shyong Chen. A novel application of grey systemtheory to information security (Part I). Computer Standards&Interfaces, Volume31, Issue2,February2009, Pages277-281
[252] Erdal Kayacan, Baris Ulutas, Okyay Kaynak. Grey system theory-based models in timeseries prediction. Expert Systems with Applications, Volume37, Issue2, March2010, Pages1784-1789
[253]路杨,何欣,杜娟娟.基于灰色理论的故障案例检索算法[J].计算机工程,2008,34(9):28-29.
[254] Tsung-Ying Tsai Kai-Wei Chang. Calvin Yu-Chian Chen. iScreen: world’s first cloud-computing web server for virtual screening and de novo drug design based on TCM database@Taiwan. J Comput Aided Mol Des (2011)25:525–531