Named Entity Recognition in Chinese Electronic Medical Records Based on the Model of Bidirectional Long Short-Term Memory with a Conditional Random Field Layer

Luqi Li; Li Hou

doi:10.3233/SHTI190516

Named Entity Recognition in Chinese Electronic Medical Records Based on the Model of Bidirectional Long Short-Term Memory with a Conditional Random Field Layer

Stud Health Technol Inform. 2019 Aug 21:264:1524-1525. doi: 10.3233/SHTI190516.

Authors

Luqi Li¹, Li Hou¹

Affiliation

¹ Institute of Medical Information and Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.

PMID: 31438213
DOI: 10.3233/SHTI190516

Abstract

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(BiLSTM-CRF). In terms of simultaneously identifying 5 types of clinical entities from CCKS2018 Chinese EHRs corpus, the BiLSTM-CRF model finally achieved better performance than the baseline CRF model (F-score of 84.23% vs 82.49%).

Keywords: Data Mining; Electronic Health Records; Natural Language Processing.

MeSH terms

Asian People
Electronic Health Records*
Humans
Memory, Short-Term*