综合性医院数字化档案信息自动分类方法研究  

Research on Automatic Classification Method of Digital Archives Information in General Hospital

作　　者：孙娆[1] SUN Rao(The First People's Hospital of Lianyungang City,Lianyungang 222006 China)

机构地区：[1]江苏省连云港市第一人民医院,江苏连云港222006

出　　处：《自动化技术与应用》2023年第7期74-77,共4页Techniques of Automation and Applications

摘　　要：针对数字化医疗档案数目急剧增多,不同类型的档案信息分类变成棘手问题,为此提出基于离散核支持向量机的数字化档案信息自动分类方法。通过向量空间模型将档案文档扩展为向量空间点,将信息特征矢量与词条矢量间的夹角作为数据库与医院数字化信息的关联性,降均频与规范化处理特征词条权值评估函数,保留权值高的词条,完成数字化档案信息采集;借助梯度下降原则获得连续学习规律,运用近似法明确数据间的距离值,得到清洗后的数据大小。实验结果表明,该方法拥有极佳的分类精度,有效缩短分类所耗时长,为综合性医院数字化档案收集与管理提供借鉴。In view of the rapid increase in the number of digital medical records,the classification of different types of file information becomes a difficult problem.Therefore,an automatic classification method based on discrete kernel support vector machine is proposed.Through the vector space model,archive documents are expanded into vector space points,and the Angle between the information feature vector and the entry vector is taken as the correlation between the database and the hospital digital information.The average frequency is reduced and the feature entry weight evaluation function is processed in a standardized manner,and the entries with high weight are retained to complete the collection of digital archive information.Gradient descent principle is used to obtain the continuous learning rule,the approximate method is used to clarify the distance between the data,and the size of the data after cleaning is obtained.The experimental results show that this method has excellent classification accuracy and can effectively shorten the time consuming of classification,which provides reference for the collection and management of digital archives in general hospitals.

关 键 词：数字化档案分类 信息清洗 无监督学习 离散核支持向量机 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]