Named Entity Recognition Using BERT BiLSTM CRF for Chinese Electronic Health Records

Zhenjin Dai, Xutao Wang, Pin Ni, Yuming Li, Gangmin Li, Xuming Bai

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

115 Citations (Scopus)

Abstract

As the generation and accumulation of massive electronic health records (EHR), how to effectively extract the valuable medical information from EHR has been a popular research topic. During the medical information extraction, named entity recognition (NER) is an essential natural language processing (NLP) task. This paper presents our efforts using neural network approaches for this task. Based on the Chinese EHR offered by CCKS 2019 and the Second Affiliated Hospital of Soochow University (SAHSU), several neural models for NER, including BiLSTM, have been compared, along with two pre-trained language models, word2vec and BERT. We have found that the BERT-BiLSTM-CRF model can achieve approximately 75% F1 score, which outperformed all other models during the tests.

Original languageEnglish
Title of host publicationProceedings - 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019
EditorsQingli Li, Lipo Wang
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728148526
DOIs
Publication statusPublished - Oct 2019
Event12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019 - Huaqiao, China
Duration: 19 Oct 201921 Oct 2019

Publication series

NameProceedings - 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019

Conference

Conference12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019
Country/TerritoryChina
CityHuaqiao
Period19/10/1921/10/19

Keywords

  • BiLSTM-CRF
  • Electronic Health Records
  • Named Entity Recognition

Fingerprint

Dive into the research topics of 'Named Entity Recognition Using BERT BiLSTM CRF for Chinese Electronic Health Records'. Together they form a unique fingerprint.

Cite this