A Hybrid Model for Named Entity Recognition on Chinese Electronic Medical Records

doi:10.1145/3436819

	A Hybrid Model for Named Entity Recognition on Chinese Electronic Medical Records
	Wang, Yu 1,2,4; Sun, Yining 1,2,4; Ma, Zuchang 1,3; Gao, Lisheng 1,3; Xu, Yang 1,3
刊名	ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING
	2021-04-01
卷号	20
关键词	Named entity recognition Chinese electronic medical records neural networks hybrid models
ISSN号	2375-4699
DOI	10.1145/3436819
通讯作者	Sun, Yining(ynsun@iim.ac.cn)
英文摘要	Electronic medical records (EMRs) contain valuable information about the patients, such as clinical symptoms, diagnostic results, and medications. Named entity recognition (NER) aims to recognize entities from unstructured text, which is the initial step toward the semantic understanding of the EMRs. Extracting medical information from Chinese EMRs could be a more complicated task because of the difference between English and Chinese. Some researchers have noticed the importance of Chinese NER and used the recurrent neural network or convolutional neural network (CNN) to deal with this task. However, it is interesting to know whether the performance could be improved if the advantages of the RNN and CNN can be both utilized. Moreover, RoBERTa-WWM, as a pre-training model, can generate the embeddings with word-level features, which is more suitable for Chinese NER compared with Word2Vec. In this article, we propose a hybrid model. This model first obtains the entities identified by bidirectional long short-term memory and CNN, respectively, and then uses two hybrid strategies to output the final results relying on these entities. We also conduct experiments on raw medical records from real hospitals. This dataset is provided by the China Conference on Knowledge Graph and Semantic Computing in 2019 (CCKS 2019). Results demonstrate that the hybrid model can improve performance significantly.
资助项目	Major Special Project of Anhui Science and Technology Department[18030801133] ; Science and Technology Service Network Initiative[KFJ-STS-ZDTP-079]
WOS关键词	INFORMATION
WOS研究方向	Computer Science
语种	英语
出版者	ASSOC COMPUTING MACHINERY
WOS记录号	WOS:000648719600017
资助机构	Major Special Project of Anhui Science and Technology Department ; Science and Technology Service Network Initiative
内容类型	期刊论文
源URL	[http://ir.hfcas.ac.cn:8080/handle/334002/122254]
专题	中国科学院合肥物质科学研究院
通讯作者	Sun, Yining
作者单位	1.Inst Intelligent Machines, Hefei, Anhui, Peoples R China 2.Chinese Acad Sci, Inst Intelligent Machines, Hefei Inst Phys Sci, AnHui Prov Key Lab Med Phys & Technol, Hefei, Anhui, Peoples R China 3.Chinese Acad Sci, Inst Intelligent Machines, Hefei Inst Phys Sci, Hefei, Anhui, Peoples R China 4.Univ Sci & Technol China, Hefei, Anhui, Peoples R China
推荐引用方式 GB/T 7714	Wang, Yu,Sun, Yining,Ma, Zuchang,et al. A Hybrid Model for Named Entity Recognition on Chinese Electronic Medical Records[J]. ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING,2021,20.
APA	Wang, Yu,Sun, Yining,Ma, Zuchang,Gao, Lisheng,&Xu, Yang.(2021).A Hybrid Model for Named Entity Recognition on Chinese Electronic Medical Records.ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING,20.
MLA	Wang, Yu,et al."A Hybrid Model for Named Entity Recognition on Chinese Electronic Medical Records".ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING 20(2021).