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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6497281/ https://www.ncbi.nlm.nih.gov/pubmed/31048840 http://dx.doi.org/10.1371/journal.pone.0216046 |
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author | Dong, Xishuang Chowdhury, Shanta Qian, Lijun Li, Xiangfang Guan, Yi Yang, Jinfeng Yu, Qiubin |
author_facet | Dong, Xishuang Chowdhury, Shanta Qian, Lijun Li, Xiangfang Guan, Yi Yang, Jinfeng Yu, Qiubin |
author_sort | Dong, Xishuang |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-6497281 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-64972812019-05-17 Deep learning for named entity recognition on Chinese electronic medical records: Combining deep transfer learning with multitask bi-directional LSTM RNN Dong, Xishuang Chowdhury, Shanta Qian, Lijun Li, Xiangfang Guan, Yi Yang, Jinfeng Yu, Qiubin PLoS One Research Article 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. Public Library of Science 2019-05-02 /pmc/articles/PMC6497281/ /pubmed/31048840 http://dx.doi.org/10.1371/journal.pone.0216046 Text en © 2019 Dong et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Dong, Xishuang Chowdhury, Shanta Qian, Lijun Li, Xiangfang Guan, Yi Yang, Jinfeng Yu, Qiubin Deep learning for named entity recognition on Chinese electronic medical records: Combining deep transfer learning with multitask bi-directional LSTM RNN |
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 |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6497281/ https://www.ncbi.nlm.nih.gov/pubmed/31048840 http://dx.doi.org/10.1371/journal.pone.0216046 |
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