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Assistant diagnosis with Chinese electronic medical records based on CNN and BiLSTM with phrase-level and word-level attentions
BACKGROUND: Inferring diseases related to the patient’s electronic medical records (EMRs) is of great significance for assisting doctor diagnosis. Several recent prediction methods have shown that deep learning-based methods can learn the deep and complex information contained in EMRs. However, they...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7275511/ https://www.ncbi.nlm.nih.gov/pubmed/32503424 http://dx.doi.org/10.1186/s12859-020-03554-x |
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author | Wang, Tong Xuan, Ping Liu, Zonglin Zhang, Tiangang |
author_facet | Wang, Tong Xuan, Ping Liu, Zonglin Zhang, Tiangang |
author_sort | Wang, Tong |
collection | PubMed |
description | BACKGROUND: Inferring diseases related to the patient’s electronic medical records (EMRs) is of great significance for assisting doctor diagnosis. Several recent prediction methods have shown that deep learning-based methods can learn the deep and complex information contained in EMRs. However, they do not consider the discriminative contributions of different phrases and words. Moreover, local information and context information of EMRs should be deeply integrated. RESULTS: A new method based on the fusion of a convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) with attention mechanisms is proposed for predicting a disease related to a given EMR, and it is referred to as FCNBLA. FCNBLA deeply integrates local information, context information of the word sequence and more informative phrases and words. A novel framework based on deep learning is developed to learn the local representation, the context representation and the combination representation. The left side of the framework is constructed based on CNN to learn the local representation of adjacent words. The right side of the framework based on BiLSTM focuses on learning the context representation of the word sequence. Not all phrases and words contribute equally to the representation of an EMR meaning. Therefore, we establish the attention mechanisms at the phrase level and word level, and the middle module of the framework learns the combination representation of the enhanced phrases and words. The macro average f-score and accuracy of FCNBLA achieved 91.29 and 92.78%, respectively. CONCLUSION: The experimental results indicate that FCNBLA yields superior performance compared with several state-of-the-art methods. The attention mechanisms and combination representations are also confirmed to be helpful for improving FCNBLA’s prediction performance. Our method is helpful for assisting doctors in diagnosing diseases in patients. |
format | Online Article Text |
id | pubmed-7275511 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-72755112020-06-08 Assistant diagnosis with Chinese electronic medical records based on CNN and BiLSTM with phrase-level and word-level attentions Wang, Tong Xuan, Ping Liu, Zonglin Zhang, Tiangang BMC Bioinformatics Methodology Article BACKGROUND: Inferring diseases related to the patient’s electronic medical records (EMRs) is of great significance for assisting doctor diagnosis. Several recent prediction methods have shown that deep learning-based methods can learn the deep and complex information contained in EMRs. However, they do not consider the discriminative contributions of different phrases and words. Moreover, local information and context information of EMRs should be deeply integrated. RESULTS: A new method based on the fusion of a convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) with attention mechanisms is proposed for predicting a disease related to a given EMR, and it is referred to as FCNBLA. FCNBLA deeply integrates local information, context information of the word sequence and more informative phrases and words. A novel framework based on deep learning is developed to learn the local representation, the context representation and the combination representation. The left side of the framework is constructed based on CNN to learn the local representation of adjacent words. The right side of the framework based on BiLSTM focuses on learning the context representation of the word sequence. Not all phrases and words contribute equally to the representation of an EMR meaning. Therefore, we establish the attention mechanisms at the phrase level and word level, and the middle module of the framework learns the combination representation of the enhanced phrases and words. The macro average f-score and accuracy of FCNBLA achieved 91.29 and 92.78%, respectively. CONCLUSION: The experimental results indicate that FCNBLA yields superior performance compared with several state-of-the-art methods. The attention mechanisms and combination representations are also confirmed to be helpful for improving FCNBLA’s prediction performance. Our method is helpful for assisting doctors in diagnosing diseases in patients. BioMed Central 2020-06-05 /pmc/articles/PMC7275511/ /pubmed/32503424 http://dx.doi.org/10.1186/s12859-020-03554-x Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data. |
spellingShingle | Methodology Article Wang, Tong Xuan, Ping Liu, Zonglin Zhang, Tiangang Assistant diagnosis with Chinese electronic medical records based on CNN and BiLSTM with phrase-level and word-level attentions |
title | Assistant diagnosis with Chinese electronic medical records based on CNN and BiLSTM with phrase-level and word-level attentions |
title_full | Assistant diagnosis with Chinese electronic medical records based on CNN and BiLSTM with phrase-level and word-level attentions |
title_fullStr | Assistant diagnosis with Chinese electronic medical records based on CNN and BiLSTM with phrase-level and word-level attentions |
title_full_unstemmed | Assistant diagnosis with Chinese electronic medical records based on CNN and BiLSTM with phrase-level and word-level attentions |
title_short | Assistant diagnosis with Chinese electronic medical records based on CNN and BiLSTM with phrase-level and word-level attentions |
title_sort | assistant diagnosis with chinese electronic medical records based on cnn and bilstm with phrase-level and word-level attentions |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7275511/ https://www.ncbi.nlm.nih.gov/pubmed/32503424 http://dx.doi.org/10.1186/s12859-020-03554-x |
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