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

Chenyuan Hu, Shuoyan Zhang, Tianyu Gu, Zhuangzhi Yan and Jiehui Jiang
Additional contact information
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

IJERPH, 2022, vol. 19, issue 9, 1-13

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.

Keywords: syndrome differentiation; multi-task learning; joint learning; deep learning (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1660-4601/19/9/5601/pdf (application/pdf)
https://www.mdpi.com/1660-4601/19/9/5601/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:19:y:2022:i:9:p:5601-:d:808682

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().