Article(id=1242141949394756533, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1242141088937485147, articleNumber=null, orderNo=null, doi=10.3981/j.issn.1000-7857.2024.05.00473, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1698854400000, receivedDateStr=2023-11-02, revisedDate=1724947200000, revisedDateStr=2024-08-30, acceptedDate=null, acceptedDateStr=null, onlineDate=1734105600000, onlineDateStr=2024-12-14, pubDate=1731427200000, pubDateStr=2024-11-13, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1734105600000, onlineIssueDateStr=2024-12-14, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1774079318614, creator=sys-migrate, updateTime=1774079318614, updator=sys-migrate, issue=Issue{id=1242141088937485147, tenantId=1146029695717560320, journalId=1146031591421210625, year='2024', volume='42', issue='21', pageStart='1', pageEnd='188', issueExtLink='null', onlineDate='null', pubDate='1731427200000', pubDateStr='2024-11-13', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=null, issueType=-1, specialIssue=null, createTime=1774079113466, creator='sys-migrate', updateTime=1774079113466, updator='sys-migrate', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=149, endPage=162, ext={EN=ArticleExt(id=1242141956311167112, articleId=1242141949394756533, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=The study on the formula characteristics of Zhengan Xifeng decoction in the treatment of hypertension based on machine learning models, columnId=1242141098387252106, journalTitle=Science & Technology Review, columnName=Exclusive: The paradigm and application of clinical research in traditional Chinese medicine, runingTitle=null, highlight=null, articleAbstract=The high prevalence of hypertension and its wide range of complications have made it a significant risk factor for cardiovascular disease mortality.Zhengan xifeng decoction (ZXD) is a classic traditional Chinese medicine prescription used in the clinical treatment of hypertension.However, there is a lack of systematic analysis of its composition rules and biological effects.In order to further explore the clinical application characteristics of ZXD in the treatment of hypertension, this study established a dataset based on the electronic medical records of hypertension patients from the Affiliated Hospital of Shandong University of Traditional Chinese Medicine from 2011 to 2019.Apriori algorithm was used to quantify the compatibility strength of the Chinese medicines in ZXD, while convolutional neural networks were employed to quantify the dosage characteristics.Topological feature analysis of protein interactions was used to examine biological characteristics.The outcome was then input into k-nearest neighbors, support vector machines, gradient boosting decision trees, Bayesian networks, and logistic regression models to evaluate the efficacy of each model, reflecting the composition rules and mechanisms of action of ZXD from multiple dimensions.The results showed that the k-nearest neighbors algorithm performed the best among the five models (AUC=93.5%), identifying 87 effective combinations, validating the mechanisms by which ZXD regulates cytokines, reduces inflammation, and corrects metabolic disorders.External testing indicated that this research could be extrapolated to other diseases.This study demonstrates that the model effectively integrates convolutional neural network data and network topology data, incorporating dosage information and biological characteristics into the exploration of Chinese medicine combinations.It complements traditional association rule models and has good general applicability and extrapolation capability, providing an interdisciplinary research approach for mining multimodal Chinese medicine datasets., authors=HUAN Jiaming1 , CHEN Xiaoqing2 , YANG Wenqing3 , LI Jie3 , HUA Zhen4 , WANG Yifei4 , LI Yunlun1,4,5 , authorsList=HUAN Jiaming, CHEN Xiaoqing, YANG Wenqing, LI Jie, HUA Zhen, WANG Yifei, LI Yunlun, authorCompany=1. First School of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan 250355, China;1 , 陈晓晴2 , 杨雯晴3 , 李洁3 , 滑振4 , 王怡斐4 , 李运伦1,4,5 , authorsList=郇家铭, 陈晓晴, 杨雯晴, 李洁, 滑振, 王怡斐, 李运伦, authorCompany=1. 山东中医药大学第一临床医学院, 济南 250355;
科技导报
| 专题：中医临床研究的范式与应用 2024, 42(21): 149-162
基于机器学习模型分析镇肝熄风汤治疗高血压病的配伍特征研究
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郇家铭, 陈晓晴, 杨雯晴, 李洁, 滑振, 王怡斐, 李运伦
作者信息
The study on the formula characteristics of Zhengan Xifeng decoction in the treatment of hypertension based on machine learning models
HUAN Jiaming, CHEN Xiaoqing, YANG Wenqing, LI Jie, HUA Zhen, WANG Yifei, LI Yunlun
Affiliations
出版时间: 2024-11-13
doi: 10.3981/j.issn.1000-7857.2024.05.00473
文章导航
以2011—2019年山东中医药大学附属医院的高血压电子病历建立数据集，用Apriori算法量化镇肝熄风汤组成中药的配伍强度，卷积神经网络量化中药的剂量特征信息，蛋白质相互作用的拓扑特征分析生物特征信息，并将上述信息输入K最邻近分类算法、支持向量机、梯度递增决策树、贝叶斯网络和逻辑回归模型，评估各个模型效力，从多个维度体现镇肝熄风汤的组成配伍规律和作用机制。结果表明，K最邻近分类算法在5个模型中结果最优（AUC=93.5%），共获得了87组有效配伍，验证了镇肝熄风汤配伍调节细胞因子、减少炎症反应和代谢紊乱的作用机制。同时，外部测试表明此研究可外推至其他疾病，表明此模型可有效融合卷积神经网络数据和网络拓扑数据，将剂量信息和生物特征融入中药配伍挖掘，对传统的关联规则模型进行补充，具有良好的普遍适用性和外推性，为多模态的中药数据集挖掘提供了一种跨学科研究方法。
机器学习
/
真实世界数据
/
数据挖掘
/
镇肝熄风汤
/
高血压
The high prevalence of hypertension and its wide range of complications have made it a significant risk factor for cardiovascular disease mortality.Zhengan xifeng decoction (ZXD) is a classic traditional Chinese medicine prescription used in the clinical treatment of hypertension.However, there is a lack of systematic analysis of its composition rules and biological effects.In order to further explore the clinical application characteristics of ZXD in the treatment of hypertension, this study established a dataset based on the electronic medical records of hypertension patients from the Affiliated Hospital of Shandong University of Traditional Chinese Medicine from 2011 to 2019.Apriori algorithm was used to quantify the compatibility strength of the Chinese medicines in ZXD, while convolutional neural networks were employed to quantify the dosage characteristics.Topological feature analysis of protein interactions was used to examine biological characteristics.The outcome was then input into k-nearest neighbors, support vector machines, gradient boosting decision trees, Bayesian networks, and logistic regression models to evaluate the efficacy of each model, reflecting the composition rules and mechanisms of action of ZXD from multiple dimensions.The results showed that the k-nearest neighbors algorithm performed the best among the five models (AUC=93.5%), identifying 87 effective combinations, validating the mechanisms by which ZXD regulates cytokines, reduces inflammation, and corrects metabolic disorders.External testing indicated that this research could be extrapolated to other diseases.This study demonstrates that the model effectively integrates convolutional neural network data and network topology data, incorporating dosage information and biological characteristics into the exploration of Chinese medicine combinations.It complements traditional association rule models and has good general applicability and extrapolation capability, providing an interdisciplinary research approach for mining multimodal Chinese medicine datasets.
machine learning
/
real world data
/
data mining
/
zhengan xifeng decoction
/
hypertension
郇家铭, 陈晓晴, 杨雯晴, 李洁, 滑振, 王怡斐, 李运伦.
基于机器学习模型分析镇肝熄风汤治疗高血压病的配伍特征研究.
科技导报,
2024
, 42
(21)
: 149
-162
.
DOI: 10.3981/j.issn.1000-7857.2024.05.00473
HUAN Jiaming, CHEN Xiaoqing, YANG Wenqing, LI Jie, HUA Zhen, WANG Yifei, LI Yunlun.
The study on the formula characteristics of Zhengan Xifeng decoction in the treatment of hypertension based on machine learning models[J].
Science & Technology Review ,
2024
, 42
(21)
: 149
-162
.
DOI: 10.3981/j.issn.1000-7857.2024.05.00473
2024年第42卷第21期
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文章信息
doi: 10.3981/j.issn.1000-7857.2024.05.00473
接收时间：2023-11-02
首发时间：2024-12-14
出版时间：2024-11-13
收稿日期：2023-11-02
修回日期：2024-08-30
https://castjournals.cast.org.cn/joweb/kjdb/CN/10.3981/j.issn.1000-7857.2024.05.00473
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2种不同金属材料的力学参数
科 属数 种数 占总种数比例 属 种数 占总种数比例 鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78 小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39 多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39 红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87 小菇属 Mycena 11 5.26 光柄菇属 Pluteus 5 2.39 红菇属 Russula 17 8.13 栓菌属 Trametes 5 2.39
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