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基于数据挖掘的中药方剂治疗慢性肾脏病分析

Data Mining-Based Analysis of Chinese Medicinal Herb Formulae in Chronic Kidney Disease Treatment.

作者信息

Xia Ping, Gao Kun, Xie Jiadong, Sun Wei, Shi Ming, Li Wei, Zhao Jing, Yan Jin, Liu Qiong, Zheng Min, Wang Xin, Wu Qijing, Zhou Enchao, Chen Jihong, Xv Lingdong, He Weiming

机构信息

Division of Nephrology, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing 210029, China.

School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing 210029, China.

出版信息

Evid Based Complement Alternat Med. 2020 Jan 24;2020:9719872. doi: 10.1155/2020/9719872. eCollection 2020.


DOI:10.1155/2020/9719872
PMID:32047530
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7003280/
Abstract

BACKGROUND: Traditional Chinese medicine (TCM) has long been used to treat chronic kidney disease (CKD) in Asia. Its effectiveness and safety for CKD treatment have been confirmed in documented studies. However, the prescription rule of formulae for Chinese medicinal herbs is complicated and remains uncharacterized. Thus, we used data mining technology to evaluate the treatment principle and coprescription pattern of these formulae in CKD TCM treatment. METHODS: Data on patients with CKD were obtained from the outpatient system of a TCM hospital. We established a Chinese herb knowledge base based on the Chinese Pharmacopoeia and the Chinese Materia Medica. Then, following extraction of prescription information, we deweighted and standardized each prescribed herb according to the knowledge base to establish a database of CKD treatment formulae. We analyzed the frequency with which individual herbs were prescribed, as well as their properties, tastes, meridian tropisms, and categories. Then, we evaluated coprescription patterns and assessed medication rules by performing association rule learning, cluster analysis, and complex network analysis. RESULTS: We retrospectively analyzed 299 prescriptions of 166 patients with CKD receiving TCM treatment. The most frequently prescribed core herbs for CKD treatment were Rhizoma Dioscoreae (Shanyao), Spreading Hedyotis Herb (Baihuasheshecao), Root of Snow of June (Baimagu), Radix Astragali (Huangqi), Poria (Fulin), Rhizoma Atractylodis Macrocephalae (Baizhu), Radix Pseudostellariae (Taizishen), and Fructus Corni (Shanzhuyu). The TCM properties of the herbs were mainly being warm, mild, and cold. The tastes of the herbs were mainly sweet, followed by bitter. The main meridian tropisms were Spleen Meridian of Foot-Taiyin, Liver Meridian of Foot-Jueyi, Lung Meridian of Hand-Taiyin, Stomach Meridian of Foot-Yangming, and Kidney Meridian of Foot-Shaoyin. The top three categories were deficiency-tonifying, heat-clearing, and dampness-draining diuretic. CONCLUSION: Using an integrated analysis method, we confirmed that the primary TCM pathogeneses of kidney disease were deficiency and dampness-heat. The primary treatment principles were tonifying deficiency and eliminating dampness-heat.

摘要

背景:长期以来,中药一直用于治疗亚洲的慢性肾脏病(CKD)。其治疗CKD的有效性和安全性已在文献研究中得到证实。然而,中药方剂的处方规律复杂,仍未明确。因此,我们使用数据挖掘技术来评估这些方剂在CKD中医治疗中的治疗原则和配伍模式。 方法:从一家中医医院的门诊系统获取CKD患者的数据。我们基于《中国药典》和《中药学》建立了一个中药知识库。然后,在提取处方信息后,我们根据知识库对每一味处方药进行去重和标准化,以建立CKD治疗方剂数据库。我们分析了单味药的用药频率及其药性、药味、归经和类别。然后,我们通过进行关联规则学习、聚类分析和复杂网络分析来评估配伍模式并评估用药规律。 结果:我们回顾性分析了166例接受中医治疗的CKD患者的299张处方。CKD治疗中最常用的核心药物是山药、白花蛇舌草、六月雪、黄芪、茯苓、白术、太子参和山茱萸。这些药物的中医属性主要为温、平、寒。药味主要为甘,其次为苦。主要归经为足太阴脾经、足厥阴肝经、手太阴肺经、足阳明胃经和足少阴肾经。前三类为补虚、清热、利水渗湿。 结论:通过综合分析方法,我们证实了肾病的主要中医病机为虚证和湿热证。主要治疗原则为补虚和清除湿热。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2dd/7003280/c2299b0e0bf7/ECAM2020-9719872.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2dd/7003280/016d80a9b31f/ECAM2020-9719872.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2dd/7003280/6b6200be0eb5/ECAM2020-9719872.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2dd/7003280/351bf9254b54/ECAM2020-9719872.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2dd/7003280/4355135f6f61/ECAM2020-9719872.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2dd/7003280/db0a5a62c952/ECAM2020-9719872.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2dd/7003280/c2299b0e0bf7/ECAM2020-9719872.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2dd/7003280/016d80a9b31f/ECAM2020-9719872.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2dd/7003280/6b6200be0eb5/ECAM2020-9719872.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2dd/7003280/351bf9254b54/ECAM2020-9719872.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2dd/7003280/4355135f6f61/ECAM2020-9719872.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2dd/7003280/db0a5a62c952/ECAM2020-9719872.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2dd/7003280/c2299b0e0bf7/ECAM2020-9719872.006.jpg

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