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 |
| Sumario: | 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%). |
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