摘要
目的采用Apriori算法与系统聚类的数据挖掘技术对中药方剂治疗血液病的用药规律进行分析,梳理常用的药物种类,总结药物间的配伍规律和关联规则,为临床血液病治疗的药物组方选择和中成药开发提供数据参考。方法在《中医方剂大辞典》中以"血证""虚劳""急劳""温病""热劳"5个病证作为关键词筛选治疗血液病的方剂,同时在中国知网和万方数据库采用主题"血液病"与全文"中药"进行高级检索,筛选1979——2019年有关中药治疗血液病的文献。根据支持度、置信度、提升度,对治疗血液病的中药方剂的高频次中药、配伍规律、核心中药组合、相互关系网络及聚类结果进行分析。结果共筛选获得100个方剂和中成药制剂,涉及中药64种,总用药频次772,使用频次≥10的中药有25味,频次最高的5味中药是丹参、人参、党参、黄芪和地黄;治疗血液病的中药药性以温(频次291,37.79%)、寒(频次228,29.61%)为主,药味以甘(频次486,41.82%)、苦(频次362,31.15%)为主;其中使用频次较高的核心药物组合包括川芎-当归、白芍-当归、熟地黄-当归、桃仁-当归等7个药对组合和桃仁-川芎-当归、桃仁-红花-当归、红花-川芎-当归等24个3药组合,关联规则分析发现了16种具有潜在配伍关系的白血病治疗药物组合,其中提升度4.7以上的药物组合有4个;聚类分析结果表明,高频使用的单味药物之间,具有关联性的药物组合有8组;高频药物复杂网络分析表明,当归-白芍、当归-茯苓药对和当归-白术-黄芪3药组合间的联系密切。结论治疗血液病高频药物和核心组方中以补虚药、活血药和清热药为主,益气养血、活血化瘀和清热凉血为中医药血液病的主要治法,为恶性血液病的临床用药和新药开发提供理论依据。
Objective The data mining technology based on Apriori algorithm and systematic clustering was performed to analyze the medication regularity of traditional Chinese medicine prescriptions for the treatment of hematological malignancies, sort out the commonly used medicines, summarize the drug compatibility rules and clarify the clustered relationships, with view to providing basic data for the clinical application and development of proprietary Chinese medicines targeting hematological diseases. Methods The five syndromes of "blood syndrome", "consumptive disease", "acute consumptive disease", "warm disease", "pyretic consumptive disease" were selected as key words to find possible prescriptions for the treatment of hematological diseases from Dictionary of Traditional Chinese Medicine Prescriptions. In addition, the "blood disease" and "Chinese medicine" was used as keywords to retrieve articles involving hematological disease therapy from the China National Knowledge Infrastructure and Wanfang Database from 1979 to 2019. The high-frequency traditional Chinese medicines, compatibility application rules, core traditional Chinese medicine combinations, relationship network diagrams, as well as the clustering results of traditional Chinese medicine prescriptions were analyzed according to values of support, confidence and lift, respectively. Results A total of 100 prescriptions and proprietary Chinese medicine preparations targeting hematological diseases were obtained, involving 64 kinds of traditional Chinese medicines with a total frequency of 772. Moreover, 25 Chinese medicines were identified as commonly used drugs with a frequency of ≥ 10. The traditional Chinese medicines with the highest frequency included Salviae Miltiorrhiza Radix, Ginseng Radix, Codonopsis Radix, Astragali Radix and Rehmanniae Radix. In addition, our results indicated that the main traditional Chinese medicine treating hematological diseases belongs to the warm(291 times, 37.79%) and cold drugs(228 times, 29.61%) on medicinal property,
作者
林九国
史梦瑶
金磊
胡培豪
葛宇清
程汝滨
LIN Jiu-guo;SHI Meng-yao;JIN Lei;HU Pei-hao;GE Yu-qing;CHENG Ru-bin(College of Pharmaceutical Science,Zhejiang Chinese Medical University,Hangzhou 310053,China;The First Affiliated Hospital,Zhejiang Chinese Medical University,Hangzhou 310006,China)
出处
《中草药》
CAS
CSCD
北大核心
2021年第4期1073-1082,共10页
Chinese Traditional and Herbal Drugs
基金
国家自然科学基金项目(81673755)
浙江省自然科学基金项目(LY17H290010)。
关键词
血液病
中药方剂
数据挖掘
用药规律
核心药物组合
hematological disorders
traditional Chinese medicine prescriptions
data mining
medication rules
core drug combination
