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基于数据挖掘的重症肌无力中医证型及用药特点研究

The Reseach of the Type and Characteristics of the Drug of Myatheina Gravis Based on Data Mining

作者: 专业:社会医学与卫生事业管理 导师:赵文光 年度:2015 学位:硕士  院校: 广州中医药大学

Keywords

Myasthenia gravis, Chinese literature, data mining, type of syndrome, medication

        目的:运用统计分析、数据挖掘等相关技术对现代不同医家诊治重症肌无力文献医案中的信息进行分析,通过较为全面的搜集整理病案,并录入数据,形成重症肌无力数据库,统计重症肌无力发病人群的年龄、性别、临床分型、临床症状、用药等基本信息,分析重症肌无力中医证候分类、药-症关系及所用药对等内容,归纳整理出重症肌无力的中医证侯诊治规律。方法:1.从图书馆馆藏目录及相关数据库中检索到有关诊治重症肌无力的现代医案或文献,根据纳入及排除标准,收集中医各家诊治重症肌无力的经验与验案177例。2.将医案内容分解为年龄、性别等基本信息与临床分型、症状及中医用药等诊疗信息,并分别建立相应数据库。3.对原始数据进行数据标准化和数据清洗等,形成适合数据分析的数据形式。4.采用spss统计软件对数据库中性别、年龄、临床症状、西医分型、常用中药等变量进行频数统计分析,运用因子分析及聚类分析方法对症状信息进行分析,归纳总结重症肌无力证候分类。5.采用spss clementine数据挖掘软件,运用关联规则算法对重症肌无力症状、用药及其相互关系进行数据挖掘分析,探讨重症肌无力中医诊治相关规律。结果:1.频数统计结果(1)重症肌无力病例男女比例为1:1.16,女性患者多于男性患者。(2)发病年龄有两个高峰,第一个高峰是21—40岁(占42.9%),第二个高峰在41—60岁(占25.4%)(3)临床分型频数分布为眼肌型64例,占36.1%,中度全身型62例,占35.1%,轻度全身型34例,占19.2%,迟发重症型5例,占2.8%,占35.1%;重度激进型11例,占1.2%,伴肌肉萎缩型1例,占0.6%。(4)常见症状有眼睑下垂、四肢无力、倦怠、纳呆、复视、面色苍白、咀嚼无力、吞咽困难等。对于舌脉症状来说,舌淡、舌红、舌胖、苔薄、苔白、脉细、脉沉、等都是出现频数相对多的症状。(5)常用中药有黄芪、白术、甘草、当归、升麻、党参、柴胡、陈皮、茯苓等。2.聚类分析结果第一类别:包括纳呆,便溏,面色苍白,肢冷,舌淡,舌胖,舌红,苔薄,苔白,苔黄,脉沉第二类别:包括眼睑疲劳,复视,失眠,舌淡,脉弦第三类别:包括头晕,消瘦,口干,舌红,苔薄,脉数第四类别:包括四肢无力,倦怠,少气懒言,咀嚼无力,吞咽困难,饮水反呛,声音低嘶3.关联规则分析结果(1)头晕、眼球活动受限、复视、倦怠、纳呆、便溏与眼睑下垂的关联度较大。肌肉痿软、咀嚼无力、吞咽困难、声音低嘶与四肢无力关联度最大。倦怠、四肢无力,纳呆、倦怠,面色苍白、纳呆,复视、四肢无力等各主要症状组合与眼睑下垂关系密切。(2)党参、黄芪、白术、当归、甘草、升麻、柴胡具有较强的关联度。升麻和柴胡具有较强的关联度。(3)纳呆、倦怠、面色苍白、眼睑下垂、复视、四肢无力、舌淡、苔薄、苔白、脉细、脉沉等症状都与黄芪、白术有较强的关联度。结论1.重症肌无力证型可归纳为四类,脾胃虚弱型、肝肾亏虚型、气阴两虚型、脾肾两虚型。2.同一脏腑病变的相关症状关联度较大。眼睑下垂、四肢无力是本病发病的常见症状,基本上其他症状的出现都是由此发展而来。3.本病症治疗以补中益气汤、四君子汤基本组方为主,补中益气,升阳举陷。辅以健脾益气、补益肝肾之品。另从疏风通络、镇肝熄风、活血通络角度运用川芎、僵蚕等药物。黄芪、白术两味药在治疗重症肌无力用药中为主要药物。葛根在本病的治疗中有一定的特色。
    ObjectiveAnalysis the information of literature basis by modern different doctor make a diagnosis and give treatment of myasthenia gravis,by using statistical analysis, data mining and other related technologies. Through the comprehen-sive collection medical record, and input data, form the database of myasthenia gravis,Statistical myasthenia gravis occur among age, sex, clinical classifi-cation,clinical symptoms and medication. Analysis of TCM syndrome classifi-cation,medicine the relationship between disease and drug use peer content of myasthenia gravis, sums up the law of TCM syndrome diagnosis and treatment of myasthenia gravis.Methods1.From the library catalogue and related database retrieval to modern basis or the literature about diagnosis and treatment of myasthenia gravis, according to include and exclude standards, collect each make a diagnosis and give treatment of traditional Chinese medicine experience with 177 cases of proven case of myasthenia gravis.2.Will the basis content is decomposed into the basic information such as age, gender and western medicine and TCM syndromes, symptoms and drug information such as diagnosis and treatment of traditional Chinese medicine,and entry information, establishing database of myasthenia gravis disease.3. The data pretreatment, including the data standardization, data measur-ement and data cleaning, etc.,to prepare for statistical analysis and data mining.4.Using SPSS statistical software for myasthenia gravis database in ge-nder,age,clinical symptoms, western medicine classification, commonly used traditional Chinese medicine, such as variable frequency of statistical ana -lysis, using the factor analysis and clustering analysis method to analyze the symptom information, summarizes myasthenia gravis syndrome classificati-on.5. Use SPSS software clementine data mining, using association rule algo-rithm to myasthenia gravis symptoms and the relationship between drug use and its analysis of the data mining, discusses related laws of diagnosis and treatment of myasthenia gravis in TCM.Results1. The frequency statistics results(1)Myasthenia gravis case male to female ratio of 1:1.16, with more women than men in patients.(2)The onset age, there are two peak, the first peak is 21-40 years old (42.9%),the second peak in 41-60 years old (25.4%).(3) The clinical classification type frequency distribution for the eye muscle type 64 cases, accounting for 36.1%, moderate body type 62 cases, accounting for 35.1%, mild systemic model 34 cases, accounted for 19.2%, late heavy USES in 5 cases,2.8%,35.1%;Severe radical in 11 cases, accounted for 1.2%, with muscle atrophy type in 1 case, accounting for 0.6%.(4)Common symptoms are drooping eyelids, limb weakness,fatigue,stay, diplopia,pale face, chew, muscle weakness, difficulty swallowing, etc. For tongue veins symptoms, pale tonguered tongue, tongue fat, moss thin white, pulse fine,pulse heavy are relatively more frequency symptoms.(5)Commonly used traditional Chinese medicine astragalus, atractylodes, licorice, Chinese angelica, codonopsis, radix bupleuri, cohosh, dried tangerine or orange peel.poria cocos.2. The cluster analysis resultsThe first category:including stay, loose stools,pale complexion,cold limbs, pale tongue, tongue fat, red tongue, thin moss, moss white and yellow moss, pulse heavy.The second category:including eye fatigue,diplopia,insomnia,pale tongue, pulse string.The third category including dizziness, angular, dry mouth, red tongue, moss thin, pulse number.The fourth category:including limb weakness, fatigue, less gas lazy words, chewing weakness, difficulty swallowing, drinking water choke, low voice HSS.3. The association rules analysis results(1) Limited dizziness, eye movements, diplopia, burnout, stay, loose stools, and drooping eyelids correlation is larger. Muscle impotent soft, chewing weakness, difficulty swallowing,low voice HSS and limbs weakness correlation is the largest. Burnout, limb weakness, stay, and tired, pale face, stay, diplopia, limb weakness, such as the main symptom combination closely associated with drooping eyelids.(2) Dangshen, astragalus, atractylodes, angelica, radix bupleuri, liquorice, cohosh has strong correlation. Cohosh and radix bupleuri with strong correlation.(3) Stay, burnout,pale face, ptosis, diplopia,limb weakness, pale tongue, moss, moss thin white, pulse fine, the symptom such as pulse heavy all have strong correlation with astragalus, atractylodes. Conclusion1. The syndrome characteristics of myasthenia gravis type certificate can be grouped into fou categories,weak spleen and stomach, liver and kidney deficiency, qi and Yin deficiency type, spleen kidney both deficiency type.2. The symptoms associated with a greater degree in the same organs related lesions. Ptosis, weakness is a common symptom of the disease, and other symptoms are basically evolved therefrom.3. This disease treatment to make up for in yiqi decoction, sijunzi decoction, the basic formula is given priority to, fill in beneficial gas, Yangju got stuck. Accompanied by spleen yiqi, the benefit of the liver and kidney. The other from liver extinguish wind breeze t2dm, town, invigorate the circulation of t2dm Angle using rhizoma ligustici wallichii, batryticated silkworm drugs. Astragalus, atractylodes two herbs in the treatment of myasthenia gravis as the main drug in the drug. Puerarin in the treatment of the disease have certain characteristics.
        

基于数据挖掘的重症肌无力中医证型及用药特点研究

摘要3-5
Abstract5-7
引言10-11
第一章 文献研究11-25
    第一节 中国传统医学对重症肌无力的认识11-15
        一、对病名的认识11
        二、对病因病机的认识11-14
        三、重症肌无力的治疗14-15
    第二节 西方医学对于重症肌无力的认识与研究15-19
        一、病因及发病机制15-17
        二、治疗17-19
    第三节 数据挖掘技术及其在中医药领域的应用19-25
        一、数据挖掘技术19-23
        二、数据挖掘技术在中医药领域的应用23-25
第二章 177例重症肌无力文献案例收集及分析25-28
    第一节 文献数据库的建立25-26
        一、数据来源及选择25-26
        二、数据预处理26
    第二节 研究方法26-28
        一、频数分析26
        二、因子分析26-27
        三、聚类分析27
        四、关联分析27-28
第三章 研究结果28-39
    第一节 频数分析结果28-30
        一、性别28
        二、年龄28
        三、临床分型28-29
        四、临床症状29-30
        五、常用中药30
    第二节 症状数据分析结果30-34
        一、因子分析提取症状群结果30-31
        二、症状群聚类归纳证候结果31-32
        三、症状关联规则分析结果32-34
    第三节 中药数据分析结果34-35
    第四节 症-药关系分析结果35-39
第四章 分析与讨论39-46
    第一节 一般临床资料分析39
        一、性别与年龄39
        二、临床分型39
    第二节 证候特点分析39-42
        一、症状分析39-40
        二、聚类结果分析40-41
        三、关联规则分析41-42
    第三节 常用中药分析42-44
        一、中药频数分析42-44
        二、中药关联规则分析44
    第四节 症—药关联规则分析44-45
    第五节 证治用药总结45-46
结语46-48
参考文献48-51
附录51-53
    附录1:在校期间发表论文情况51-52
    附录2:致谢52-53
附件53
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