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Multi-Task Joint Learning Model for Chinese Word Segmentation and Syndrome Differentiation in Traditional Chinese Medicine

Author

Listed:
  • Chenyuan Hu

    (School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China)

  • Shuoyan Zhang

    (School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China)

  • Tianyu Gu

    (School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China)

  • Zhuangzhi Yan

    (Institute of Biomedical Engineering, School of Life Science, Shanghai University, Shanghai 200444, China)

  • Jiehui Jiang

    (Institute of Biomedical Engineering, School of Life Science, Shanghai University, Shanghai 200444, China)

Abstract

Evidence-based treatment is the basis of traditional Chinese medicine (TCM), and the accurate differentiation of syndromes is important for treatment in this context. The automatic differentiation of syndromes of unstructured medical records requires two important steps: Chinese word segmentation and text classification. Due to the ambiguity of the Chinese language and the peculiarities of syndrome differentiation, these tasks pose a daunting challenge. We use text classification to model syndrome differentiation for TCM, and use multi-task learning (MTL) and deep learning to accomplish the two challenging tasks of Chinese word segmentation and syndrome differentiation. Two classic deep neural networks—bidirectional long short-term memory (Bi-LSTM) and text-based convolutional neural networks (TextCNN)—are fused into MTL to simultaneously carry out these two tasks. We used our proposed method to conduct a large number of comparative experiments. The experimental comparisons showed that it was superior to other methods on both tasks. Our model yielded values of accuracy, specificity, and sensitivity of 0.93, 0.94, and 0.90, and 0.80, 0.82, and 0.78 on the Chinese word segmentation task and the syndrome differentiation task, respectively. Moreover, statistical analyses showed that the accuracies of the non-joint and joint models were both within the 95% confidence interval, with p v a l u e < 0.05. The experimental comparison showed that our method is superior to prevalent methods on both tasks. The work here can help modernize TCM through intelligent differentiation.

Suggested Citation

  • Chenyuan Hu & Shuoyan Zhang & Tianyu Gu & Zhuangzhi Yan & Jiehui Jiang, 2022. "Multi-Task Joint Learning Model for Chinese Word Segmentation and Syndrome Differentiation in Traditional Chinese Medicine," IJERPH, MDPI, vol. 19(9), pages 1-13, May.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:9:p:5601-:d:808682
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    References listed on IDEAS

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    1. David Cyranoski, 2018. "Why Chinese medicine is heading for clinics around the world," Nature, Nature, vol. 561(7724), pages 448-450, September.
    2. Fortunato Pesarin & Luigi Salmaso, 2010. "The permutation testing approach: a review," Statistica, Department of Statistics, University of Bologna, vol. 70(4), pages 481-509.
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    Cited by:

    1. Senqi Yang & Xuliang Duan & Zeyan Xiao & Zhiyao Li & Yuhai Liu & Zhihao Jie & Dezhao Tang & Hui Du, 2022. "Sentiment Classification of Chinese Tourism Reviews Based on ERNIE-Gram+GCN," IJERPH, MDPI, vol. 19(20), pages 1-20, October.
    2. Shangyi Yan & Jingya Wang & Zhiqiang Song, 2022. "Microblog Sentiment Analysis Based on Dynamic Character-Level and Word-Level Features and Multi-Head Self-Attention Pooling," Future Internet, MDPI, vol. 14(8), pages 1-19, July.

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