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An attention-based deep learning model for clinical named entity recognition of Chinese electronic medical records
BACKGROUND: Clinical named entity recognition (CNER) is important for medical information mining and establishment of high-quality knowledge map. Due to the different text features from natural language and a large number of professional and uncommon clinical terms in Chinese electronic medical reco...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6894110/ https://www.ncbi.nlm.nih.gov/pubmed/31801540 http://dx.doi.org/10.1186/s12911-019-0933-6 |
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author | Li, Luqi Zhao, Jie Hou, Li Zhai, Yunkai Shi, Jinming Cui, Fangfang |
author_facet | Li, Luqi Zhao, Jie Hou, Li Zhai, Yunkai Shi, Jinming Cui, Fangfang |
author_sort | Li, Luqi |
collection | PubMed |
description | BACKGROUND: Clinical named entity recognition (CNER) is important for medical information mining and establishment of high-quality knowledge map. Due to the different text features from natural language and a large number of professional and uncommon clinical terms in Chinese electronic medical records (EMRs), there are still many difficulties in clinical named entity recognition of Chinese EMRs. It is of great importance to eliminate semantic interference and improve the ability of autonomous learning of internal features of the model under the small training corpus. METHODS: From the perspective of deep learning, we integrated the attention mechanism into neural network, and proposed an improved clinical named entity recognition method for Chinese electronic medical records called BiLSTM-Att-CRF, which could capture more useful information of the context and avoid the problem of missing information caused by long-distance factors. In addition, medical dictionaries and part-of-speech (POS) features were also introduced to improve the performance of the model. RESULTS: Based on China Conference on Knowledge Graph and Semantic Computing (CCKS) 2017 and 2018 Chinese EMRs corpus, our BiLSTM-Att-CRF model finally achieved better performance than other widely-used models without additional features(F1-measure of 85.4% in CCKS 2018, F1-measure of 90.29% in CCKS 2017), and achieved the best performance with POS and dictionary features (F1-measure of 86.11% in CCKS 2018, F1-measure of 90.48% in CCKS 2017). In particular, the BiLSTM-Att-CRF model had significant effect on the improvement of Recall. CONCLUSIONS: Our work preliminarily confirmed the validity of attention mechanism in discovering key information and mining text features, which might provide useful ideas for future research in clinical named entity recognition of Chinese electronic medical records. In the future, we will explore the deeper application of attention mechanism in neural network. |
format | Online Article Text |
id | pubmed-6894110 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-68941102019-12-11 An attention-based deep learning model for clinical named entity recognition of Chinese electronic medical records Li, Luqi Zhao, Jie Hou, Li Zhai, Yunkai Shi, Jinming Cui, Fangfang BMC Med Inform Decis Mak Research BACKGROUND: Clinical named entity recognition (CNER) is important for medical information mining and establishment of high-quality knowledge map. Due to the different text features from natural language and a large number of professional and uncommon clinical terms in Chinese electronic medical records (EMRs), there are still many difficulties in clinical named entity recognition of Chinese EMRs. It is of great importance to eliminate semantic interference and improve the ability of autonomous learning of internal features of the model under the small training corpus. METHODS: From the perspective of deep learning, we integrated the attention mechanism into neural network, and proposed an improved clinical named entity recognition method for Chinese electronic medical records called BiLSTM-Att-CRF, which could capture more useful information of the context and avoid the problem of missing information caused by long-distance factors. In addition, medical dictionaries and part-of-speech (POS) features were also introduced to improve the performance of the model. RESULTS: Based on China Conference on Knowledge Graph and Semantic Computing (CCKS) 2017 and 2018 Chinese EMRs corpus, our BiLSTM-Att-CRF model finally achieved better performance than other widely-used models without additional features(F1-measure of 85.4% in CCKS 2018, F1-measure of 90.29% in CCKS 2017), and achieved the best performance with POS and dictionary features (F1-measure of 86.11% in CCKS 2018, F1-measure of 90.48% in CCKS 2017). In particular, the BiLSTM-Att-CRF model had significant effect on the improvement of Recall. CONCLUSIONS: Our work preliminarily confirmed the validity of attention mechanism in discovering key information and mining text features, which might provide useful ideas for future research in clinical named entity recognition of Chinese electronic medical records. In the future, we will explore the deeper application of attention mechanism in neural network. BioMed Central 2019-12-05 /pmc/articles/PMC6894110/ /pubmed/31801540 http://dx.doi.org/10.1186/s12911-019-0933-6 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Li, Luqi Zhao, Jie Hou, Li Zhai, Yunkai Shi, Jinming Cui, Fangfang An attention-based deep learning model for clinical named entity recognition of Chinese electronic medical records |
title | An attention-based deep learning model for clinical named entity recognition of Chinese electronic medical records |
title_full | An attention-based deep learning model for clinical named entity recognition of Chinese electronic medical records |
title_fullStr | An attention-based deep learning model for clinical named entity recognition of Chinese electronic medical records |
title_full_unstemmed | An attention-based deep learning model for clinical named entity recognition of Chinese electronic medical records |
title_short | An attention-based deep learning model for clinical named entity recognition of Chinese electronic medical records |
title_sort | attention-based deep learning model for clinical named entity recognition of chinese electronic medical records |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6894110/ https://www.ncbi.nlm.nih.gov/pubmed/31801540 http://dx.doi.org/10.1186/s12911-019-0933-6 |
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