Deep learning for named entity recognition on Chinese electronic medical records: Combining deep transfer learning with multitask bi-directional LSTM RNN.
Specific entity terms such as disease, test, symptom, and genes in Electronic Medical Record (EMR) can be extracted by Named Entity Recognition (NER). However, limited resources of labeled EMR pose a great challenge for mining medical entity terms. In this study, a novel multitask bi-directional RNN...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2019-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0216046 |
| _version_ | 1798032727700668416 |
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| author | Xishuang Dong Shanta Chowdhury Lijun Qian Xiangfang Li Yi Guan Jinfeng Yang Qiubin Yu |
| author_facet | Xishuang Dong Shanta Chowdhury Lijun Qian Xiangfang Li Yi Guan Jinfeng Yang Qiubin Yu |
| author_sort | Xishuang Dong |
| collection | DOAJ |
| description | Specific entity terms such as disease, test, symptom, and genes in Electronic Medical Record (EMR) can be extracted by Named Entity Recognition (NER). However, limited resources of labeled EMR pose a great challenge for mining medical entity terms. In this study, a novel multitask bi-directional RNN model combined with deep transfer learning is proposed as a potential solution of transferring knowledge and data augmentation to enhance NER performance with limited data. The proposed model has been evaluated using micro average F-score, macro average F-score and accuracy. It is observed that the proposed model outperforms the baseline model in the case of discharge datasets. For instance, for the case of discharge summary, the micro average F-score is improved by 2.55% and the overall accuracy is improved by 7.53%. For the case of progress notes, the micro average F-score and the overall accuracy are improved by 1.63% and 5.63%, respectively. |
| first_indexed | 2024-04-11T20:17:26Z |
| format | Article |
| id | doaj.art-ddc50afbe0d34d03a6f84fab00b613a5 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-11T20:17:26Z |
| publishDate | 2019-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-ddc50afbe0d34d03a6f84fab00b613a52022-12-22T04:04:54ZengPublic Library of Science (PLoS)PLoS ONE1932-62032019-01-01145e021604610.1371/journal.pone.0216046Deep learning for named entity recognition on Chinese electronic medical records: Combining deep transfer learning with multitask bi-directional LSTM RNN.Xishuang DongShanta ChowdhuryLijun QianXiangfang LiYi GuanJinfeng YangQiubin YuSpecific entity terms such as disease, test, symptom, and genes in Electronic Medical Record (EMR) can be extracted by Named Entity Recognition (NER). However, limited resources of labeled EMR pose a great challenge for mining medical entity terms. In this study, a novel multitask bi-directional RNN model combined with deep transfer learning is proposed as a potential solution of transferring knowledge and data augmentation to enhance NER performance with limited data. The proposed model has been evaluated using micro average F-score, macro average F-score and accuracy. It is observed that the proposed model outperforms the baseline model in the case of discharge datasets. For instance, for the case of discharge summary, the micro average F-score is improved by 2.55% and the overall accuracy is improved by 7.53%. For the case of progress notes, the micro average F-score and the overall accuracy are improved by 1.63% and 5.63%, respectively.https://doi.org/10.1371/journal.pone.0216046 |
| spellingShingle | Xishuang Dong Shanta Chowdhury Lijun Qian Xiangfang Li Yi Guan Jinfeng Yang Qiubin Yu Deep learning for named entity recognition on Chinese electronic medical records: Combining deep transfer learning with multitask bi-directional LSTM RNN. PLoS ONE |
| title | Deep learning for named entity recognition on Chinese electronic medical records: Combining deep transfer learning with multitask bi-directional LSTM RNN. |
| title_full | Deep learning for named entity recognition on Chinese electronic medical records: Combining deep transfer learning with multitask bi-directional LSTM RNN. |
| title_fullStr | Deep learning for named entity recognition on Chinese electronic medical records: Combining deep transfer learning with multitask bi-directional LSTM RNN. |
| title_full_unstemmed | Deep learning for named entity recognition on Chinese electronic medical records: Combining deep transfer learning with multitask bi-directional LSTM RNN. |
| title_short | Deep learning for named entity recognition on Chinese electronic medical records: Combining deep transfer learning with multitask bi-directional LSTM RNN. |
| title_sort | deep learning for named entity recognition on chinese electronic medical records combining deep transfer learning with multitask bi directional lstm rnn |
| url | https://doi.org/10.1371/journal.pone.0216046 |
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