Named Entity Recognition in Chinese Electronic Medical Records Based on the Model of Bidirectional Long Short-Term Memory with a Conditional Random Field Layer...MEDINFO 2019, the 17th World Congress on Medical and Health Informatics, August 25-30, 2019, Lyon, France.
Named entity recognition in electronic medical records is of great significance to the construction of medical knowledge maps. This paper proposes a model of bidirectional Long Short-Term Memory with a conditional random field layer(BiLSTMCRF). In terms of simultaneously identifying 5 types of clini...
| Publicado en: | Studies in Health Technology & Informatics Vol. 264; pp. 1524 - 1526 |
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| Autores principales: | , |
| Formato: | equations & formulas proceedings research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2019
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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=148538349&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 148538349 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2019 vid: 264 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 148538349 148538349 148538349 10.3233/SHTI190516 148538349 ppf: 1524 ppct: 2 formats: tig: atl: Named Entity Recognition in Chinese Electronic Medical Records Based on the Model of Bidirectional Long Short-Term Memory with a Conditional Random Field Layer...MEDINFO 2019, the 17th World Congress on Medical and Health Informatics, August 25-30, 2019, Lyon, France. aug: au: Luqi Li Li Hou affil: Institute of Medical Information and Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China sug: subj: Natural Language Processing Electronic Health Records China Long Short-Term Memory Data Mining Information Retrieval Human Congresses and Conferences France France China Decision Making, Clinical Decision Support Systems, Clinical Neural Networks (Computer) Deep Learning Chinese Persons Descriptive Statistics Funding Source ab: Named entity recognition in electronic medical records is of great significance to the construction of medical knowledge maps. This paper proposes a model of bidirectional Long Short-Term Memory with a conditional random field layer(BiLSTMCRF). In terms of simultaneously identifying 5 types of clinical entities from CCKS2018 Chinese EHRs corpus, the BiLSTMCRF model finally achieved better performance than the baseline CRF model (F-score of 84.23% vs 82.49%). pubtype: Academic Journal doctype: equations & formulas proceedings research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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