Electronic Medical Record Entity Recognition via Machine Reading Comprehension and Biaffine
The entity recognition of Chinese electronic medical record is of great significance to medical decision-making. The main process of entity recognition is sequence tagging, which has problems such as nested entity and boundary prediction. In this paper, we proposed a NER method called Bert-MRC-Biaff...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2021/1640837 |
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| _version_ | 1832561365818015744 |
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| author | Jun Cao Xian Zhou Wangping Xiong Ming Yang Jianqiang Du Yanyun Yang Tianci Li |
| author_facet | Jun Cao Xian Zhou Wangping Xiong Ming Yang Jianqiang Du Yanyun Yang Tianci Li |
| author_sort | Jun Cao |
| collection | DOAJ |
| description | The entity recognition of Chinese electronic medical record is of great significance to medical decision-making. The main process of entity recognition is sequence tagging, which has problems such as nested entity and boundary prediction. In this paper, we proposed a NER method called Bert-MRC-Biaffine, which formulates the NER as an MRC task. The approach of the machine reading comprehension framework is to introduce prior knowledge, the query about entities. The biaffine mechanism scores pair start and end tokens in a sentence so that the model is able to predict named entities accurately. The proposed method outperforms from the electronic medical record dataset, called CCKS2017 data, and the TCM dataset. We also remove components to evaluate the contribution of individual components of our model. Experiments on two datasets demonstrate the effectiveness of our model. |
| format | Article |
| id | doaj-art-77f67e9e984f4b4ea90909c76fa6d295 |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-77f67e9e984f4b4ea90909c76fa6d2952025-02-03T01:25:10ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2021-01-01202110.1155/2021/16408371640837Electronic Medical Record Entity Recognition via Machine Reading Comprehension and BiaffineJun Cao0Xian Zhou1Wangping Xiong2Ming Yang3Jianqiang Du4Yanyun Yang5Tianci Li6School of Computer, Jiangxi University of Traditional Chinese Medicine, Nanchang, ChinaSchool of Computer, Jiangxi University of Traditional Chinese Medicine, Nanchang, ChinaSchool of Computer, Jiangxi University of Traditional Chinese Medicine, Nanchang, ChinaSchool of Computer, Jiangxi University of Traditional Chinese Medicine, Nanchang, ChinaSchool of Computer, Jiangxi University of Traditional Chinese Medicine, Nanchang, ChinaSchool of Computer, Jiangxi University of Traditional Chinese Medicine, Nanchang, ChinaSchool of Computer, Jiangxi University of Traditional Chinese Medicine, Nanchang, ChinaThe entity recognition of Chinese electronic medical record is of great significance to medical decision-making. The main process of entity recognition is sequence tagging, which has problems such as nested entity and boundary prediction. In this paper, we proposed a NER method called Bert-MRC-Biaffine, which formulates the NER as an MRC task. The approach of the machine reading comprehension framework is to introduce prior knowledge, the query about entities. The biaffine mechanism scores pair start and end tokens in a sentence so that the model is able to predict named entities accurately. The proposed method outperforms from the electronic medical record dataset, called CCKS2017 data, and the TCM dataset. We also remove components to evaluate the contribution of individual components of our model. Experiments on two datasets demonstrate the effectiveness of our model.http://dx.doi.org/10.1155/2021/1640837 |
| spellingShingle | Jun Cao Xian Zhou Wangping Xiong Ming Yang Jianqiang Du Yanyun Yang Tianci Li Electronic Medical Record Entity Recognition via Machine Reading Comprehension and Biaffine Discrete Dynamics in Nature and Society |
| title | Electronic Medical Record Entity Recognition via Machine Reading Comprehension and Biaffine |
| title_full | Electronic Medical Record Entity Recognition via Machine Reading Comprehension and Biaffine |
| title_fullStr | Electronic Medical Record Entity Recognition via Machine Reading Comprehension and Biaffine |
| title_full_unstemmed | Electronic Medical Record Entity Recognition via Machine Reading Comprehension and Biaffine |
| title_short | Electronic Medical Record Entity Recognition via Machine Reading Comprehension and Biaffine |
| title_sort | electronic medical record entity recognition via machine reading comprehension and biaffine |
| url | http://dx.doi.org/10.1155/2021/1640837 |
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