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A model of integrating convolution and BiGRU dual-channel mechanism for Chinese medical text classifications

Resource type
Authors/contributors
Title
A model of integrating convolution and BiGRU dual-channel mechanism for Chinese medical text classifications
Abstract
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.
Publication
PLOS ONE
Volume
18
Issue
3
Pages
e0282824
Date
16 Mar 2023
Journal Abbr
PLOS ONE
Language
en
ISSN
1932-6203
Accessed
4/11/23, 1:09 PM
Library Catalog
PLoS Journals
Extra
Publisher: Public Library of Science
Citation
Li, X., Zhang, Y., Jin, J., Sun, F., Li, N., & Liang, S. (2023). A model of integrating convolution and BiGRU dual-channel mechanism for Chinese medical text classifications. PLOS ONE, 18(3), e0282824. https://doi.org/10.1371/journal.pone.0282824