中国科技术语 ›› 2021, Vol. 23 ›› Issue (2): 42-48.doi: 10.3969/j.issn.1673-8578.2021.02.006

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癫痫临床诊疗数据规范化研究

张妮楠1(), 曹馨宇2, 林睿凡1, 王斌1, 史华新3, 周洪伟1, 谢琪4   

  • 收稿日期:2020-06-24 修回日期:2020-11-15 出版日期:2021-04-25 发布日期:2021-04-07
  • 作者简介:张妮楠(1992—),博士在读,现就读于中国中医科学院中医临床基础医学研究所,研究方向为中医真实世界研究方法与技术。硕士毕业选题为“全国名中医余瀛鳌治疗癫痫的通治方研究”。本文系对医案中医学术语的整理。通信方式:znnzhangninan@163.com
    谢琪(1971—),医学博士,研究员,博士生导师,现任中国中医科学院学术管理处处长兼数据中心副主任,世界中医药学会联合会真实世界研究专委会副会长兼秘书长,世界中医药学会联合会伦理审查委员会常务理事,全国语言与术语标准化技术委员会(SAC/TC62)委员。研究方向为中医真实世界研究方法与技术,参加了中医真实世界研究技术平台“中医临床科研信息共享系统”应用评价与优化,糖网、更年期综合征、非小细胞肺癌、癫痫等中医临床研究,获软件著作权4项和专利2项,组织制订了《TCACM 022—2017中医真实世界研究技术规范通则》等团体标准,近年研究关注中医潜镇化痰法治疗癫痫的有效性循证评价及作用机制、中医医学本体构建技术征收应用。
  • 基金资助:
    中国中医科学院中央级公益性科研院所科研基本业务费自主选题“余瀛鳌传承工作室建设(第二期)——余瀛鳌先生的专病诊疗方案研究”(ZZ140505);科技部国家重点研发计划课题“多维多层多态中医药知识图谱及时空演化模型研究”(2017YFB1002302)

Study on Standardization of Clinical Data of Epilepsy Diagnosis and Treatment

ZHANG Ninan1(), CAO Xinyu2, LIU Ruifan1, WANG Bin1, SHI Huaxin3, ZHOU Hongwei1, XIE Qi4   

  • Received:2020-06-24 Revised:2020-11-15 Online:2021-04-25 Published:2021-04-07

摘要:

中医常采用自然语言描述疾病症状,导致症状命名不统一,影响数据挖掘分析和临床疗效评价结果。该研究主要以中医临床专病诊疗数据为研究对象,示范性地探索和整理了临床诊疗数据中术语该如何规范化的问题,从语义分析的角度来规范化整理症状术语,在首选术语制定过程中引入术语属性和术语间关系的概念,解决了症状间多词一义、多义一词的问题。参照ICD-11中文版整理了疾病诊断术语;参照《中国药典》规范了中药药名;按照“治愈”“好转”“未愈”分类整理疗效评价术语。最终整理获得症状术语558条,其中首选术语164条,同义术语394条;诊断术语23条;疗效评价术语21条,规范后的数据可用于数据挖掘分析。

关键词: 癫痫诊疗数据, 数据规范化, 首选术语, 同义术语, 术语属性

Abstract:

Chinese medicine often uses natural language to describe the symptoms of the disease, which leads to inconsistent naming of the symptoms and affects the results of data mining analysis and clinical efficacy evaluation. Based on the diagnosis and treatment data of clinical special diseases of a famous Chinese medicine practitioner, this study explored on how to standardize the terminology in clinical diagnosis and treatment data. From the perspective of semantic analysis of standardization symptoms terms, we introduced the concept of the term properties and the relationships between terms in the process of establishing the preferred terms, and solved the problem of multi-word meaning and polysemy between symptoms. Also, we sorted out the disease diagnosis terms according to the ICD-11 Chinese version, standardized the names of traditional Chinese medicines according to Chinese Pharmacopoeia, and sorted the curative effect evaluation terms according to “cure”, “improvement”, and “unhealed”. We finally obtained 558 symptom terms, among which 164 were preferred terms, and obtained 394 synonymous terms, 23 diagnosis terms, 21 terms of efficacy evaluation. The standardized data can be used for data mining analysis.

Key words: epilepsy diagnosis and treatment data, data standardization, preferred term, synonymous term, term properties

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