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Online biomedical named entities recognition by data and knowledge-driven model
Lulu Cao1; Chaochen Wu3; Guan Luo2; Chao Guo4; Anni Zheng2
Source PublicationArtificial Intelligence In Medicine
ISSN0933-3657
2024
Volume150Pages:102813
Corresponding AuthorWu, Chaochen(wuchaochen2021@ruc.edu.cn) ; Luo, Guan(gluo@nlpr.ia.ac.cn)
SubtypeArticle
Abstract

Named entity recognition (NER) is an important task for the natural language processing of biomedical text. Currently, most NER studies standardized biomedical text, but NER for unstandardized biomedical
text draws less attention from researchers. Named entities in online biomedical text exist with errors and polymorphisms, which negatively impact NER models’ performance and impede support from knowledge
representation methods. In this paper, we propose a neural network method that can effectively recognize entities in unstandardized online medical/health text. We introduce a new pre-training scheme that uses largescale online question-answering pairs to enhance transformers’ model capacity on online biomedical text. Moreover, we supply models with knowledge representations from a knowledge base called multi-channel knowledge labels, and this method overcomes the restriction from languages, like Chinese, that require word segmentation tools to represent knowledge. Our model outperforms other baseline methods significantly in experiments on a dataset for Chinese online medical entity recognition and achieves state-of-the-art results.

KeywordBiomedical named entity recognition Neural network Pre-training Knowledge representation Online text
DOI10.1016/j.artmed.2024.102813
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[82201988] ; Peking University People's Hospital Research and Develop-ment Funds[RDJP2022-01]
Funding OrganizationNational Natural Science Foundation of China ; Peking University People's Hospital Research and Develop-ment Funds
WOS Research AreaComputer Science ; Engineering ; Medical Informatics
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Biomedical ; Medical Informatics
WOS IDWOS:001197546600001
PublisherELSEVIER
Sub direction classification图像视频处理与分析
planning direction of the national heavy laboratory视觉信息处理
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/57236
Collection多模态人工智能系统全国重点实验室_视频内容安全
Corresponding AuthorGuan Luo
Affiliation1.Department of Rheumatology and Immunology, Peking University People’s Hospital
2.State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation, Chinese Academy of Sciences
3.Renmin University of China
4.Department of Cardiology, Fuwai Hospital CAMS and PUMC
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Lulu Cao,Chaochen Wu,Guan Luo,et al. Online biomedical named entities recognition by data and knowledge-driven model[J]. Artificial Intelligence In Medicine,2024,150:102813.
APA Lulu Cao,Chaochen Wu,Guan Luo,Chao Guo,&Anni Zheng.(2024).Online biomedical named entities recognition by data and knowledge-driven model.Artificial Intelligence In Medicine,150,102813.
MLA Lulu Cao,et al."Online biomedical named entities recognition by data and knowledge-driven model".Artificial Intelligence In Medicine 150(2024):102813.
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