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| Assisted TCM Diagnosis and Treatment for Diabetes with Multi NLP Tasks |
Zhang Yujie1,Bai Rujiang1( ),Xu Haiyun2,Han Jing3,Zhao Mengmeng1 |
1Institute of Information Management, Shandong University of Technology, Zibo 255000, China
2School of Management, Shandong University of Technology, Zibo 255000, China
3Shandong College of Traditional Chinese Medicine, Yantai 264199, China |
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Abstract [Objective] This paper tries to provide more accurate and intelligent auxiliary reference for the diagnosis and treatment of Traditional Chinese Medicine (TCM), aiming to reduce their uncertainty and difficult to quantify issues. [Methods] First, we collected medical records of TCM for diabetes. Then, we created an auxiliary diagnosis and treatment scheme integrating multiple NLP tasks, i.e., emotion recognition and text matching. Finally, we examined our new model with the quantitative assessment of diabetes, symptom information matching, automatic symptom summarization, disease type discrimination and TCM recommendation. [Results] We conducted ten rounds of tests with the fuzzy comprehensive evaluation method. The average membership degrees of the four evaluation indices were 0.194 9, 0.314 0, 0.217 3 and 0.273 8 respectively. The maximum membership degree indicated the effectiveness of the proposed method. [Limitations] Due to the scarcity of clinical medical records, it is difficult to improve the performance of each subtask significantly. More research is needed to examine the model with data from other fields. [Conclusions] This method can effectively help doctors reduce the uncertainty as well as evaluate diagnosis and treatment.
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Received: 26 April 2021
Published: 22 February 2022
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| Fund:General Project of Social Science Planning and Research in Shandong Province(21CTQJ11) |
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Corresponding Authors:
Bai Rujiang，ORCID：0000-0003-3822-8484
E-mail: brj@sdut.edu.cn
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