基于隐结构和关联规则的名老中医辨治暑温用药规律研究
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摘要
目的:探究名老中医辨治暑温的病机、证候分型演变和用药规律。方法:以现代名老中医肺病数据库中447例暑温医案为数据源,先用Lantern3. 1. 2软件建立症状和药物的隐结构模型并进行诠释,再用SPSS Modeler 18. 0对症-症、药-药、药-症进行关联规则挖掘,最后对挖掘结果进行分析讨论。结果:447例暑温医案频次≥10的症状54种、频次≥20的药物72味,常见症状有发热、汗出、咳嗽、胸闷、口干、口渴,常用药物有茯苓、半夏、苦杏仁、陈皮、连翘、通草;症状隐结构模型得到10个隐变量,含20个隐类,药物隐结构模型得到22个隐变量,含44个隐类,症-症关联有头痛、恶寒和发热,恶心、呕吐和胸闷,发热、欲饮和口渴,咳嗽和咯痰,神昏和谵语。药-药关联有党参和甘草,藿香、泽泻和茯苓,天花粉、石斛、生地和甘草。结论:采用卫气营血和三焦辨证辨治暑温,暑温病位有肺、胃、心、脾,病理因素有气虚、阴虚、痰湿等,证型有气阴两虚、湿阻中焦、暑湿伤气等。老中医辨治暑温的常用核心药物有佩兰、茯苓泽泻、天花粉、砂仁、陈皮、白术,佩兰是化湿的基础药;核心方剂有银翘散、三仁汤、五加减正气散、沙参麦冬汤。
Objective: To explore the pathogenesis,syndrome differentiation evolution and medication rule of the old famous TCM physicians treating summer-heat. Methods: A total 447 cases of summer-heat in the database of modern famous TCM physicians were as the data source. First we used Lantern3. 1. 2 software to set up the symptoms and medication structure model and then used SPSS Modeler 18. 0 to carry out the association rules mining of symptom-symptom,drug-drug,drug-symptom,and finally analyzed the results. Results: Among 447 cases of summer-heat,the symptoms which frequency is more than 10 are 54 kinds and the drugs which frequency more than 20 are 72 kinds. The common symptoms are fever,sweating,cough,chest tightness,dry mouth and thirst. The commonly-used drugs are Fuling( poria cocos),Banxia( pinellia),Kuxingren( bitter almonds),Chenpi( dried tangerine or orange peel),Lianqiao( forsythia) and Tongcao( ricepaperplant pith). Symptoms hidden structure model got 10 hidden variables,including 20 classes. Drugs hidden structure model had 22 hidden variables,including 44 implicit classes. The disease-disease model was associated with headache,aversion to cold and fever,nausea,vomiting and chest tightness,fever,desiring drinking and thirsty,cough and phlegm,faint and delirious speech. The drug-drug model was Dangshen( codonopsis) and Gancao( licorice root),Huoxiang( Agastache rugosa),Zexie( Alisma plantago-aquatica Linn.) and Fuling( Poria cocos),Tianhuafen( root of Chinese trichosanthes),Shihu( dendrobium),Shengdi( Rehmannia glutinosa Libosch) and Gancao( licorice). Conclusion: The treatment of summer-heat can be according to Wei-Qi-Ying-Xue sydnrome differentiation or three-Jiao sydnrome differentation. The location is in lung,stomach,heart and spleen. The pathological factors include Qi deficiency,Yin deficiency,phlegm-dampness and the sydromes are Yin deficiency,dampness in the middle,summer-heat damaging Qi,etc.The common core drugs of the old Chinese medicine physicians treatment are Peilan( Eupatorium fortunei Turcz.),Fuling( poria rhizoma),Zexie( Alisma plantago-aquatica Linn.),Tianhuafen( root of Chinese trichosanthes),Sharen( Amomum villosum Lour.),Chenpi( orange peel),Baizhu( Atractylodes macrocephala Koidz). Peilan( Eupatorium fortunei Turcz.) is the basic medicine of resolving dampness. The core prescriptions are Yinqiao Powder,Sanren Decoction,Modified Zhengqi Powder and Shashen Maidong Decoction.
Objective: To explore the pathogenesis,syndrome differentiation evolution and medication rule of the old famous TCM physicians treating summer-heat. Methods: A total 447 cases of summer-heat in the database of modern famous TCM physicians were as the data source. First we used Lantern3. 1. 2 software to set up the symptoms and medication structure model and then used SPSS Modeler 18. 0 to carry out the association rules mining of symptom-symptom,drug-drug,drug-symptom,and finally analyzed the results. Results: Among 447 cases of summer-heat,the symptoms which frequency is more than 10 are 54 kinds and the drugs which frequency more than 20 are 72 kinds. The common symptoms are fever,sweating,cough,chest tightness,dry mouth and thirst. The commonly-used drugs are Fuling( poria cocos),Banxia( pinellia),Kuxingren( bitter almonds),Chenpi( dried tangerine or orange peel),Lianqiao( forsythia) and Tongcao( ricepaperplant pith). Symptoms hidden structure model got 10 hidden variables,including 20 classes. Drugs hidden structure model had 22 hidden variables,including 44 implicit classes. The disease-disease model was associated with headache,aversion to cold and fever,nausea,vomiting and chest tightness,fever,desiring drinking and thirsty,cough and phlegm,faint and delirious speech. The drug-drug model was Dangshen( codonopsis) and Gancao( licorice root),Huoxiang( Agastache rugosa),Zexie( Alisma plantago-aquatica Linn.) and Fuling( Poria cocos),Tianhuafen( root of Chinese trichosanthes),Shihu( dendrobium),Shengdi( Rehmannia glutinosa Libosch) and Gancao( licorice). Conclusion: The treatment of summer-heat can be according to Wei-Qi-Ying-Xue sydnrome differentiation or three-Jiao sydnrome differentation. The location is in lung,stomach,heart and spleen. The pathological factors include Qi deficiency,Yin deficiency,phlegm-dampness and the sydromes are Yin deficiency,dampness in the middle,summer-heat damaging Qi,etc.The common core drugs of the old Chinese medicine physicians treatment are Peilan( Eupatorium fortunei Turcz.),Fuling( poria rhizoma),Zexie( Alisma plantago-aquatica Linn.),Tianhuafen( root of Chinese trichosanthes),Sharen( Amomum villosum Lour.),Chenpi( orange peel),Baizhu( Atractylodes macrocephala Koidz). Peilan( Eupatorium fortunei Turcz.) is the basic medicine of resolving dampness. The core prescriptions are Yinqiao Powder,Sanren Decoction,Modified Zhengqi Powder and Shashen Maidong Decoction.
引文
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[2]陈丽平,李建生,蔡永敏,等.中医肺病数据库构建及挖掘的思路与方法[J].中华中医药杂志,2017,32(12):5530-5534.
[3]国家技术监督局.中医临床诊疗术语[M].北京:中国标准出版社,1997.
[4]中医药学名词审定委员会.中医药学名词[M].北京:科学技术出版社,2005.
[5]龚燕冰,张连文,高思华,等.隐结构模型对2型糖尿病病机规律的探讨[J].世界科学技术-中医药现代化,2009,11(4):516.
[6]何伟,郭树仁,谢雁鸣,等. 826例高甘油三酯血症患者证候要素特征的隐结构模型评价[J].中国中西医结合杂志,2013,33(8):1059.
[7]陈丽平.基于隐结构模型的名老中医辨治慢性支气管炎规律研究[D].南京:南京中医药大学,2015.
[8]於实.基于关系数据库的教学评价数据的关联规则挖掘[J].科技通报,2012,28(4):99-101.
[9]陈莉平.基于关联规则的数据挖掘算法的研究与应用[J].现代电子技术,2007,20(11):71-74.
[10]叶天士,著.温热论[M].潘静娟,点校.上海:上海第二军医大学出版社,2012.
[11]叶天士,著.临证指南医案[M].宋白杨,校注.北京:中国医药科技出版社,2011.
[12]李旗历.岭南温病辨证论治特点的研究[D].北京:北京中医药大学,2010.
[13]钟赣生,主编.中药学[M].北京:中国中医药出版社,2016:188-191.
[14]王士雄,著.温热经纬[M].李顺保,金华,点校.北京:学苑出版社,2013.
[15]吴仪洛,著.本草从新[M].陆拯,赵法新,陈明显,点校.北京:中国中医药出版社,2013.