利用自然语言处理技术提升医院档案信息提取的效率  

Using natural language processing technology to improve efficiency of hospital archive information extraction

作　　者：傅天一 FU Tianyi(Shandong Public Health Clinical Center,Jinan 250101,China)

机构地区：[1]山东省公共卫生临床中心,济南250101

出　　处：《计算机应用文摘》2025年第9期94-96,共3页

摘　　要：传统的档案信息提取方法主要依赖人工操作,这不仅耗时费力,还易出现错误,影响数据的准确性和可靠性。随着自然语言处理(Natural Language Processing,NLP)技术的迅速发展,医院档案信息提取的效率得到了显著提升。文章探讨了如何应用NLP技术来提高医院档案信息提取的效率,重点介绍了文本分类、命名实体识别和关系抽取等关键技术。其中,文本分类可以自动对档案进行分类,有效组织信息;命名实体识别用于识别和提取关键信息,如患者姓名、疾病名称和药物等;关系抽取则可以揭示不同信息间的关系,帮助建立完整的信息网络。Traditional archival information extraction methods mainly rely on manual operation,which is not only time-consuming and laborious,but also prone to errors,affecting the accuracy and reliability of data.With the rapid development of Natural Language Processing(NLP)technology,the efficiency of hospital archive information extraction has been significantly improved.This paper discusses how to apply NLP technology to improve the efficiency of hospital archive information extraction,and focuses on the key technologies such as text classification,named entity recognition and relation extraction.Among them,text classification can automatically classify files and effectively organize information,Named entity recognition is used to identify and extract key information,such as patient names,disease names and drugs,Relationship extraction can reveal the relationship between different information and help build a complete information network.

关 键 词：自然语言处理 档案信息提取 文本分类 命名实体识别 关系抽取 医疗信息处理 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]