电子病历命名实体识别和实体关系抽取研究综述

杨锦锋; 于秋滨; 关毅; 蒋志鹏

doi:10.3724/SP.J.1004.2014.01537

电子病历命名实体识别和实体关系抽取研究综述

doi: 10.3724/SP.J.1004.2014.01537

1.
哈尔滨工业大学语言技术中心网络智能研究室哈尔滨 150001;
2.
哈尔滨医科大学附属第二医院病案室哈尔滨 150086

基金项目:

国家自然科学基金（60975077）资助

详细信息

作者简介:
杨锦锋哈尔滨工业大学博士研究生.主要研究方向为自然语言处理，电子病历信息抽取.E-mail：yangjinfeng2010@gmail.com

通讯作者:
关毅哈尔滨工业大学教授. 主要研究方向为智能信息检索，网络挖掘，自然语言处理，认知语言学.E-mail：guanyi@hit.edu.cn

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- 被引次数: 0
出版历程
- 收稿日期: 2013-08-30
- 修回日期: 2013-12-18
- 刊出日期: 2014-08-20

An Overview of Research on Electronic Medical Record Oriented Named Entity Recognition and Entity Relation Extraction

1.
Web Intelligence Laboratory, Language Technology Center, Harbin Institute of Technology, Harbin 150001;
2.
Medical Record Room, The 2nd Affiliated Hospital of Harbin Medical University, Harbin 150086

Funds:

Supported by National Natural Science Foundation of China (60975077)

摘要

摘要: 电子病历（Electronic medical records，EMR）产生于临床治疗过程，其中命名实体和实体关系反映了患者健康状况，包含了大量与患者健康状况密切相关的医疗知识，因而对它们的识别和抽取是信息抽取研究在医疗领域的重要扩展. 本文首先讨论了电子病历文本的语言特点和结构特点，然后在梳理了命名实体识别和实体关系抽取研究一般思路的基础上，分析了电子病历命名实体识别、实体修饰识别和实体关系抽取研究的具体任务和对应任务的主要研究方法. 本文还介绍了相关的共享评测任务和标注语料库以及医疗领域几个重要的词典和知识库等资源. 最后对这一研究领域仍需解决的问题和未来的发展方向作了展望.
- 电子病历 /
- 命名实体识别 /
- 实体关系抽取 /
- 共享评测任务
Abstract: Electronic medical records (EMRs) are generated in the process of clinical treatments. Named entities and entity relations in EMRs reflect patients' health conditions and represent patients' personalized medical knowledge. Consequently, named entity recognition and entity relation extraction on EMR are important expansion of information extraction in the medical domain. In this paper, the language characteristic and structure features of EMR narratives are firstly discussed, and then general methods for named entity recognition and relation extraction are sketched out. Furthermore, this paper introduces and analyzes the tasks and corresponding methods for named entity recognition, entity assertion recognition and relation extraction of EMR in detail. Related shared evaluation tasks and annotated corpora as well as several important dictionaries and knowledge bases are also introduced. Finally, problems to be handled and future research directions are proposed.
- Electronic medical record (EMR) /
- named entity recognition /
- entity relation extraction /
- shared task

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参考文献(132)

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