An imConvNet-based deep learning model for Chinese medical named entity recognition.
Background: With the development of current medical technology, information management becomes perfect in the medical field. Medical big data analysis is based on a large amount of medical and health data stored in the electronic medical system, such as electronic medical records and medical reports...
| Publicado en: | BMC Medical Informatics & Decision Making Vol. 22; no. 1; pp. 1 - 13 |
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| Autores principales: | , , , , , , |
| Formato: | Journal Article |
| Publicado: |
BioMed Central
11/21/2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=160326601&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 160326601 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726947 1CI0 jtl: BMC Medical Informatics & Decision Making issn: 14726947 maglogo: N pubinfo: dt: 11/21/2022 vid: 22 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 160326601 160326601 NLM36411432 10.1186/s12911-022-02049-4 NLM36411432 160326601 ppf: 1 ppct: 12 formats: tig: atl: An imConvNet-based deep learning model for Chinese medical named entity recognition. aug: au: Zheng, Yuchen Han, Zhenggong Cai, Yimin Duan, Xubo Sun, Jiangling Yang, Wei Huang, Haisong affil: Medical College, Guizhou University, 550025, Guiyang, Guizhou, China sug: subj: Nomenclature China Language Data Mining Short Portable Mental Status Questionnaire ab: Background: With the development of current medical technology, information management becomes perfect in the medical field. Medical big data analysis is based on a large amount of medical and health data stored in the electronic medical system, such as electronic medical records and medical reports. How to fully exploit the resources of information included in these medical data has always been the subject of research by many scholars. The basis for text mining is named entity recognition (NER), which has its particularities in the medical field, where issues such as inadequate text resources and a large number of professional domain terms continue to face significant challenges in medical NER.Methods: We improved the convolutional neural network model (imConvNet) to obtain additional text features. Concurrently, we continue to use the classical Bert pre-training model and BiLSTM model for named entity recognition. We use imConvNet model to extract additional word vector features and improve named entity recognition accuracy. The proposed model, named BERT-imConvNet-BiLSTM-CRF, is composed of four layers: BERT embedding layer-getting word embedding vector; imConvNet layer-capturing the context feature of each character; BiLSTM (Bidirectional Long Short-Term Memory) layer-capturing the long-distance dependencies; CRF (Conditional Random Field) layer-labeling characters based on their features and transfer rules.Results: The average F1 score on the public medical data set yidu-s4k reached 91.38% when combined with the classical model; when real electronic medical record text in impacted wisdom teeth is used as the experimental object, the model's F1 score is 93.89%. They all show better results than classical models.Conclusions: The suggested novel model (imConvNet) significantly improves the recognition accuracy of Chinese medical named entities and applies to various medical corpora. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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