Dai, Zhenjin;
Wang, Xutao;
Ni, Pin;
Li, Yuming;
Li, Gangmin;
Bai, Xuming;
(2020)
Named Entity Recognition Using BERT BiLSTM CRF for Chinese Electronic Health Records.
In: Li, Qingli and Wang, Lipo, (eds.)
2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI).
(pp. pp. 1-5).
IEEE: Suzhou, China.
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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.
| Type: | Proceedings paper |
|---|---|
| Title: | Named Entity Recognition Using BERT BiLSTM CRF for Chinese Electronic Health Records |
| Event: | 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) |
| Dates: | 19 Oct 2019 - 21 Oct 2019 |
| ISBN-13: | 978-1-7281-4852-6 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/CISP-BMEI48845.2019.8965823 |
| Publisher version: | https://doi.org/10.1109/CISP-BMEI48845.2019.896582... |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Task analysis; Medical diagnostic imaging; Bit error rate; Diseases; Biological system modeling; Tagging |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10159894 |
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