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Named Entity Recognition in Chinese Electronic Medical Records Based on the Model of Bidirectional Long Short-Term Memory with a Conditional Random Field Layer...The 17th World Congress of Medical and Health Informatics, 25-30 August 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 clin...

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Published in:Studies in Health Technology & Informatics Vol. 264; pp. 1524 - 1526
Main Authors: Luqi Li, Li Hou
Format: equations & formulas proceedings research tables/charts Journal Article
Published: Sage Publications Inc. 2019
Online Access:View this record in EBSCOhost
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      dt: 2019
      vid: 264
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      pub: Sage Publications Inc.
      place: Thousand Oaks, California
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        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...The 17th World Congress of Medical and Health Informatics, 25-30 August 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
          Data Mining
          Information Retrieval
          Long Short-Term Memory
          Congresses and Conferences France
          France
          Human
          China
          Decision Making, Clinical
          Decision Support Systems, Clinical
          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
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