基于古籍医案从方证相应探索中药干预中风失语的证治规律  

Exploration of Syndrome and Treatment Law of Post Stroke Aphasia Intervened by TCM from Prescription-syndrome Relativity Based on Medical Cases in Ancient Books

作　　者：陈健 任巧生 马亚男 陶晓华 高颖[1] 常静玲[1] CHEN Jian;REN Qiaosheng;MA Yanan;TAO Xiaohua;GAO Ying;CHANG Jingling(Dongzhimen Hospital,Beijing University of Chinese Medicine,Beijing 100700,China;School of Traditional Chinese Medicine,Beijing University of Chinese Medicine,Beijing 100029,China)

机构地区：[1]北京中医药大学东直门医院,北京100700 [2]北京中医药大学中医学院,北京100029

出　　处：《中国中医药信息杂志》2023年第8期36-42,共7页Chinese Journal of Information on Traditional Chinese Medicine

基　　金：国家重点研发计划(2019YFC1709200、2019YFC1709203、2019YFC1709204)。

摘　　要：目的挖掘古籍医案中风失语证候要素与组方用药规律,从方证相应探析本病论治思路。方法以国家重点研发计划中医古籍“病脉证并治”知识元标引数据库及《中华医典》(第5版)古籍数据库医案部古籍为主要医案来源,筛选符合要求的中风失语古籍医案,运用Python3.8编程语言提取医案的证候、处方等信息,录入Excel2019,进一步提取证素信息,形成标准证药信息数据库,运用Pandas库对数据进行规范化处理、统计描述,调用Apriori算法模块进行关联规则分析;运用Gephi0.9.7进行证素复杂网络可视化呈现与分析;运用SPSS Statistics 26.0对高频中药进行系统聚类分析。结果共纳入48部古籍的210则中风失语医案,涉及18个病性证素,总频次为495,痰、内风、热、气虚、阴虚为高频病性证素;9个病位证素,总频次为218,肝、肾、脾、心、经络为高频病位证素;中药262味,总频次为2261,频次≥16中药共40味,包括茯苓、法半夏、人参片、石菖蒲、当归等。关联规则分析得到病位-病性强关联规则10条,中药-病性强关联规则20条。复杂网络可视化分析获得5个病性证素与5个病位证素的紧密关联网络。高频中药聚类分析获得3类核心组合。结论中风失语急性期,肝热生风、痰迷心窍以清神解语汤和羚角钩藤汤主之;恢复期与后遗症期,脾虚痰阻以六君子汤主之,肝肾阴虚、痰浊上扰以地黄饮子主之。Objective To mine the syndrome factors and medication law of prescriptions of post stroke aphasia(PSA)in medical cases of ancient books;To explore the treatment idea of PSA from the perspective of prescriptionsyndrome relativity.Methods The main source of medical cases was the knowledge element indexing database of the national key research and development plan of ancient Chinese medical books“treating diseases,pulse and syndrome simultaneously”and the medical case part of ancient book database of the Chinese Medical Code(5th Edition).Qualified ancient medical records of PSA were screened.Python 3.8 programming language was used to extract the syndrome,prescription and other information of the medical case,and the information was entered into Excel 2019.Syndrome element information was further extracted to form standard syndrome drug information database.The Pandas library was used to normalize the data processing and statistical description,and the Apriori algorithm module was used to analyze the association rules.Gephi 0.9.7 was used to visualize and analyze the complex network of syndrome elements.SPSS Statistics 26.0 was used to conduct systematic cluster analysis on high-frequency Chinese materia medica.Results Totally 210 cases of PSA in 48 ancient books were included,involving 18 disease nature syndrome elements,with a total frequency of 495.Phlegm,internal wind,heat,qi deficiency and yin deficiency were high frequency disease nature syndrome element.There were 9 disease location syndrome elements,with a total frequency of 218.Liver,kidney,spleen,heart and meridians were high frequency disease location syndrome elements.262 types of Chinese materia medica had a total frequency of 2261,there were 40 types of Chinese materia medica with frequency of≥16,including Poria,Pinelliae Rhizoma Praeparatum,Ginseng Radix et Rhizoma,Acori Tatarinowii Rhizoma,and Angelicae Sinensis Radix.According to the analysis of association rules,there were 10 strong association rules of disease location-disease nature and 20

关 键 词：中风失语 古籍医案 方证相应 证素 证治规律 数据挖掘 

分 类 号：R2-05[医药卫生—中医学] R277.733