摘要
运用数据挖掘技术构建医案数据库是归纳和总结名医经验的常用方法。为了提升数据挖掘结果的使用价值,探讨现代名老中医医案数据库的构建方式,在优化数据库结构与常用字段的基础上,借助HanLP分词系统,应用结合最大概率算法的隐马尔可夫模型(HMM)分词算法进行医案文本的切分与语义标注,并通过python根据语义标注结果进行医案信息提取。该方法有助于优化医案数据结构,提升了数据库的构建效率和数据挖掘质量。
Data mining technology is a commonly used method to induct and summarize the experience of famous doctors based on the constructed database of medical cases. To promote the use of the data mining results and to explore the method suitable for constructing the database of medical cases from modern famous veteran doctors of traditional Chinese medicine(TCM), this study optimized the database structure and common fields, used HanLP word segmentation system in combination with maximum probability algorithm-based hidden Markov model(HMM) framework to perform text segmentation and semantic annotation, and extracted the data according to the annotated semantics through python technique. This method helps to optimize the data structure of medical records, and improves the efficiency of database construction and the quality of data mining.
作者
赵泽鹏
戴国华
高武霖
ZHAO Zepeng;DAI Guohua;GAO Wulin(Shandong University of Traditional Chinese Medicine,Jinan,250014;The Affiliated Hospital of Shandong University of Traditional Chinese Medicine)
出处
《中医杂志》
CSCD
北大核心
2021年第11期951-955,共5页
Journal of Traditional Chinese Medicine
基金
国家重点研发计划(2019YFC1710401)
国家自然科学基金(81774047)。
关键词
中医医案
数据库
名老中医
数据处理
medical cases of traditional Chinese medicine
database
famous doctors of traditional Chinese medicine
data processing
