A BERT-BiGRU-CRF Model for Entity Recognition of Chinese Electronic Medical Records
Because of difficulty processing the electronic medical record data of patients with cerebrovascular disease, there is little mature recognition technology capable of identifying the named entity of cerebrovascular disease. Excellent research results have been achieved in the field of named entity r...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6631837 |
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| author | Qiuli Qin Shuang Zhao Chunmei Liu |
| author_facet | Qiuli Qin Shuang Zhao Chunmei Liu |
| author_sort | Qiuli Qin |
| collection | DOAJ |
| description | Because of difficulty processing the electronic medical record data of patients with cerebrovascular disease, there is little mature recognition technology capable of identifying the named entity of cerebrovascular disease. Excellent research results have been achieved in the field of named entity recognition (NER), but there are several problems in the pre processing of Chinese named entities that have multiple meanings, of which neglecting the combination of contextual information is one. Therefore, to extract five categories of key entity information for diseases, symptoms, body parts, medical examinations, and treatment in electronic medical records, this paper proposes the use of a BERT-BiGRU-CRF named entity recognition method, which is applied to the field of cerebrovascular diseases. The BERT layer first converts the electronic medical record text into a low-dimensional vector, then uses this vector as the input to the BiGRU layer to capture contextual features, and finally uses conditional random fields (CRFs) to capture the dependency between adjacent tags. The experimental results show that the F1 score of the model reaches 90.38%. |
| format | Article |
| id | doaj-art-6b31bafb9ff24c738113d9fbc65d5c04 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-6b31bafb9ff24c738113d9fbc65d5c042025-02-03T01:04:13ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66318376631837A BERT-BiGRU-CRF Model for Entity Recognition of Chinese Electronic Medical RecordsQiuli Qin0Shuang Zhao1Chunmei Liu2School of Economics and Management, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Economics and Management, Beijing Jiaotong University, Beijing 100044, ChinaBeijing Jiaotong University Health Service Center, Beijing 100044, ChinaBecause of difficulty processing the electronic medical record data of patients with cerebrovascular disease, there is little mature recognition technology capable of identifying the named entity of cerebrovascular disease. Excellent research results have been achieved in the field of named entity recognition (NER), but there are several problems in the pre processing of Chinese named entities that have multiple meanings, of which neglecting the combination of contextual information is one. Therefore, to extract five categories of key entity information for diseases, symptoms, body parts, medical examinations, and treatment in electronic medical records, this paper proposes the use of a BERT-BiGRU-CRF named entity recognition method, which is applied to the field of cerebrovascular diseases. The BERT layer first converts the electronic medical record text into a low-dimensional vector, then uses this vector as the input to the BiGRU layer to capture contextual features, and finally uses conditional random fields (CRFs) to capture the dependency between adjacent tags. The experimental results show that the F1 score of the model reaches 90.38%.http://dx.doi.org/10.1155/2021/6631837 |
| spellingShingle | Qiuli Qin Shuang Zhao Chunmei Liu A BERT-BiGRU-CRF Model for Entity Recognition of Chinese Electronic Medical Records Complexity |
| title | A BERT-BiGRU-CRF Model for Entity Recognition of Chinese Electronic Medical Records |
| title_full | A BERT-BiGRU-CRF Model for Entity Recognition of Chinese Electronic Medical Records |
| title_fullStr | A BERT-BiGRU-CRF Model for Entity Recognition of Chinese Electronic Medical Records |
| title_full_unstemmed | A BERT-BiGRU-CRF Model for Entity Recognition of Chinese Electronic Medical Records |
| title_short | A BERT-BiGRU-CRF Model for Entity Recognition of Chinese Electronic Medical Records |
| title_sort | bert bigru crf model for entity recognition of chinese electronic medical records |
| url | http://dx.doi.org/10.1155/2021/6631837 |
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