首页|基于语义分析的中药物料智能分类模型研究

基于语义分析的中药物料智能分类模型研究

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课题组前期已建立了基于粉体物理性质的中药物料分类方法,但需涉及中药饮片的前处理、粉体制备、物理性质参数测定等较为繁琐的实验步骤.该研究采用语义分析的分词逻辑,选择已测定中药粉体物理性质并在聚类分析中分类界限清晰的36个模型中药的性状、显微鉴别特征为基础数据,建立同义词库及本地标准化语义分词数据库.运用关联规则及设定的纳排标准,筛选粉性料、纤维性料、糖性料、油性料、脆性料共计55个关键词,计算关键词的权重值,并建立关键词匹配得分的算法及单一或多元物料分类的计算规则,从而构建中药物料分类的语义分析预测模型.在36个模型中药的语义分类结果中,除太子参为多元物料外,其余35个中药的语义分类结果与基于中药粉体物理性质的聚类分析结果一致,一致率为97.22％.在模型验证中,除百部与炙益智仁外,其他中药的语义分类预测结果与粉体物理性质聚类结果一致,一致率为83.33％.表明基于语义分析的中药物料分类的方法具有可行性,为构建临方制剂工艺的智能决策技术奠定基础.
Intelligent material classification of traditional Chinese medicine based on semantic analysis
A method for material classification of traditional Chinese medicines based on the physical properties of powder has been established by our research group.This method involves pre-treatment of traditional Chinese medicine decoction pieces,powder preparation,and determination of physical properties,being cumbersome.In this study,the word segmentation logic of semantic analysis was adopted to establish the thesaurus and local standardized semantic word segmentation database with the macroscopic and microscopic characteristics of 36 model traditional Chinese medicines as the basic data.The physical properties of these medicines have been determined and the classification of these medicines is clear in the cluster analysis.A total of 55 keywords for powdery,fibrous,sugary,oily,and brittle materials were screened by association rules and the set inclusion and exclusion criteria,and the weights of the keywords were calculated.Furthermore,the algorithms of the keyword matching scores and the computation rules of the single or multiple material classification were established for building the intelligent model of semantic analysis for the material classification.The semantic classification results of the other 35 TCMs except Pseudostellariae Radix(multi-material medicine)agreed with the clustering results based on the physical properties of the powder,with an agreement rate of 97.22％.In model validation,the prediction results of semantic classification of traditional Chinese medicines were consistent with the clustering results based on the physical properties of powder,with an agreement rate of 83.33％.The results showed that the method of material classification based on semantic analysis was feasible,which laid a foundation for the development of intelligent decision-making technology for personalized traditional Chinese medicine preparations.

semantic analysismaterial classification of traditional Chinese medicinesprediction modelpersonalized traditional Chinese medicine preparations

李云琪、田文秀、薛爱乐、李文杰、胡志强、洪燕龙

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上海健康医学院附属周浦医院,上海 201318

上海中医药大学上海中医健康服务协同创新中心,上海 201203

上海中医药大学中药学院,上海 201203

中药现代制剂技术教育部工程研究中心,上海 201203

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语义分析 中药物料分类 预测模型 中药临方制剂

国家自然科学基金上海市"科技创新行动计划"技术标准项目上海中医药慢性病防治与健康服务省部共建协同创新中心项目上海市自然科学基金面上项目上海市浦东新区周浦医院院级人才培养项目上海市浦东新区卫生系统临床药学重要薄弱学科建设项目

8197349020DZ22009002021科技02-3723ZR1463500ZPRC-2023A-04PWZbr2022-11

2024

10.19540/j.cnki.cjcmm.20231220.301

中国中药杂志
中国药学会

中国中药杂志

CSTPCD北大核心
影响因子：1.718
ISSN：1001-5302
年,卷(期)：2024.49(3)
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