A model of integrating convolution and BiGRU dual-channel mechanism for Chinese medical text classifications.
Recently, a lot of Chinese patients consult treatment plans through social networking platforms, but the Chinese medical text contains rich information, including a large number of medical nomenclatures and symptom descriptions. How to build an intelligence model to automatically classify the text i...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0282824 |
| _version_ | 1797843157448130560 |
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| author | Xiaoli Li Yuying Zhang Jiangyong Jin Fuqi Sun Na Li Shengbin Liang |
| author_facet | Xiaoli Li Yuying Zhang Jiangyong Jin Fuqi Sun Na Li Shengbin Liang |
| author_sort | Xiaoli Li |
| collection | DOAJ |
| description | Recently, a lot of Chinese patients consult treatment plans through social networking platforms, but the Chinese medical text contains rich information, including a large number of medical nomenclatures and symptom descriptions. How to build an intelligence model to automatically classify the text information consulted by patients and recommend the correct department for patients is very important. In order to address the problem of insufficient feature extraction from Chinese medical text and low accuracy, this paper proposes a dual channel Chinese medical text classification model. The model extracts feature of Chinese medical text at different granularity, comprehensively and accurately obtains effective feature information, and finally recommends departments for patients according to text classification. One channel of the model focuses on medical nomenclatures, symptoms and other words related to hospital departments, gives different weights, calculates corresponding feature vectors with convolution kernels of different sizes, and then obtains local text representation. The other channel uses the BiGRU network and attention mechanism to obtain text representation, highlighting the important information of the whole sentence, that is, global text representation. Finally, the model uses full connection layer to combine the representation vectors of the two channels, and uses Softmax classifier for classification. The experimental results show that the accuracy, recall and F1-score of the model are improved by 10.65%, 8.94% and 11.62% respectively compared with the baseline models in average, which proves that our model has better performance and robustness. |
| first_indexed | 2024-04-09T17:00:15Z |
| format | Article |
| id | doaj.art-1cf1be1e65e0411295b1c58f6624decf |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-09T17:00:15Z |
| publishDate | 2023-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-1cf1be1e65e0411295b1c58f6624decf2023-04-21T05:32:54ZengPublic Library of Science (PLoS)PLoS ONE1932-62032023-01-01183e028282410.1371/journal.pone.0282824A model of integrating convolution and BiGRU dual-channel mechanism for Chinese medical text classifications.Xiaoli LiYuying ZhangJiangyong JinFuqi SunNa LiShengbin LiangRecently, a lot of Chinese patients consult treatment plans through social networking platforms, but the Chinese medical text contains rich information, including a large number of medical nomenclatures and symptom descriptions. How to build an intelligence model to automatically classify the text information consulted by patients and recommend the correct department for patients is very important. In order to address the problem of insufficient feature extraction from Chinese medical text and low accuracy, this paper proposes a dual channel Chinese medical text classification model. The model extracts feature of Chinese medical text at different granularity, comprehensively and accurately obtains effective feature information, and finally recommends departments for patients according to text classification. One channel of the model focuses on medical nomenclatures, symptoms and other words related to hospital departments, gives different weights, calculates corresponding feature vectors with convolution kernels of different sizes, and then obtains local text representation. The other channel uses the BiGRU network and attention mechanism to obtain text representation, highlighting the important information of the whole sentence, that is, global text representation. Finally, the model uses full connection layer to combine the representation vectors of the two channels, and uses Softmax classifier for classification. The experimental results show that the accuracy, recall and F1-score of the model are improved by 10.65%, 8.94% and 11.62% respectively compared with the baseline models in average, which proves that our model has better performance and robustness.https://doi.org/10.1371/journal.pone.0282824 |
| spellingShingle | Xiaoli Li Yuying Zhang Jiangyong Jin Fuqi Sun Na Li Shengbin Liang A model of integrating convolution and BiGRU dual-channel mechanism for Chinese medical text classifications. PLoS ONE |
| title | A model of integrating convolution and BiGRU dual-channel mechanism for Chinese medical text classifications. |
| title_full | A model of integrating convolution and BiGRU dual-channel mechanism for Chinese medical text classifications. |
| title_fullStr | A model of integrating convolution and BiGRU dual-channel mechanism for Chinese medical text classifications. |
| title_full_unstemmed | A model of integrating convolution and BiGRU dual-channel mechanism for Chinese medical text classifications. |
| title_short | A model of integrating convolution and BiGRU dual-channel mechanism for Chinese medical text classifications. |
| title_sort | model of integrating convolution and bigru dual channel mechanism for chinese medical text classifications |
| url | https://doi.org/10.1371/journal.pone.0282824 |
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