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基于数据挖掘探索脑出血急性期中药用药规律

Exploring the Rule of Chinese Materia Medica in Acute Stage of Intracerebral Hemorrhage Based on Data Mining
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摘要 目的:运用数据挖掘方法探索脑出血急性期的中药用药规律,为中医临床治疗本病提供组方用药思路。方法:检索自建库至2021年8月31日中国知网、万方、维普三大数据库,筛选中药治疗脑出血急性期的临床随机对照试验,采用Excel、SPSS软件进行中药使用频次、功效、性味归经、关联规则、系统聚类分析。结果:总共纳入中药处方103首、149味中药,其中大黄是高频中药之首,活血化瘀药使用频次最多,四气、五味、归经分别以温性、苦味、肝经为首,得出11条药对关联规则及2个最长距离聚类方。结论:脑出血急性期多用温性、苦味、肝经中药,以泻实结合补虚为治则,运用活血化瘀药、清热药、补虚药等组方用药。 Objective:By using data mining methods to explore the rules of Chinese materia medica in the acute phase of cerebral hemorrhage, and to provide prescriptions and medication ideas for the clinical treatment of this disease by Chinese materia medica.Methods: The databases of CNKI,Wanfang and VIP from the establishment of the database to August 31,2021 were searched to screen the clinical randomized controlled trials of Chinese materia medica in the treatment of acute intracerebral hemorrhage.The frequency, actions, properties, flavors and channel tropism of Chinese materia medica were classified by Excel and SPSS statistical software, as well as cluster analysis and correlation rule analysis.Results:103 Chinese medicine prescriptions and 149 Chinese materia medica were included.Among them, rhubarb was the first of high-frequency Chinese materia medica, and the drugs for promoting blood circulation and removing blood stasis were used the most frequently.11 drug pair correlation rules and 2 longest distance clustering parties.Conclusion:In the acute stage of cerebral hemorrhage, warm, bitter, and liver meridian herbs are often used.The treatment is to relieve solid and tonic, and use blood circulation and blood stasis medicines, clearing heat medicines, and tonic medicines.
作者 杨浩东 杨运宽 Yang Haodong;Yang Yunkuan(Acupuncture and Massage College,Chengdu University of TCM,Chengdu 610075,China;TCM Hospital of Sichuan Province,Chengdu 610075,China)
出处 《亚太传统医药》 2023年第1期159-164,共6页 Asia-Pacific Traditional Medicine
关键词 脑出血急性期 中药 用药规律 数据挖掘 Acute Stage of Intracerebral Hemorrhage Chinese Materia Medica Medication Rule Data Mining
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