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
[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.
张玉洁, 白如江, 许海云, 韩靖, 赵梦梦. 融合多自然语言处理任务的中医辅助诊疗方案研究——以糖尿病为例*[J]. 数据分析与知识发现, 2022, 6(1): 122-133.
Zhang Yujie, Bai Rujiang, Xu Haiyun, Han Jing, Zhao Mengmeng. Assisted TCM Diagnosis and Treatment for Diabetes with Multi NLP Tasks. Data Analysis and Knowledge Discovery, 2022, 6(1): 122-133.
( Cai Feifei, Ma Lixin, Hu Yuanjia, et al. “AI + Traditional Chinese Medicine”: A New Approach to the Era of Smart Traditional Chinese Medicine[J]. World Science and Technology: Modernization of Traditional Chinese Medicine, 2019, 21(9):1968-1973.)
( Xia Shujie, Yang Chaoyang, Zhou Changen, et al. Review of Application of Machine Learning Methods in the Field of Diagnostics of Traditional Chinese Medicine[J]. Journal of Guangzhou University of Traditional Chinese Medicine, 2021, 38(4):826-831.)
[3]
Chowdhury G G. Natural Language Processing[J]. Annual Review of Information Science and Technology, 2003, 37(1):51-89.
doi: 10.1002/aris.1440370103
[4]
Gopalakrishnan K, Hedayatnia B, Chen Q, et al. Topical-Chat: Towards Knowledge-Grounded Open-Domain Conversations[C]// Proceedings of the INTERSPEECH 2019. 2019:1891-1895.
[5]
Zhou L, Gao J, Li D, et al. The Design and Implementation of XiaoIce, an Empathetic Social Chatbot[J]. Computational Linguistics, 2020, 46(1):53-93.
doi: 10.1162/coli_a_00368
[6]
Guzella T S, Caminhas W M. A Review of Machine Learning Approaches to Spam Filtering[J]. Expert Systems with Applications, 2009, 36(7):10206-10222.
doi: 10.1016/j.eswa.2009.02.037
[7]
Wang H, Zhang F, Wang J, et al. Ripplenet: Propagating User Preferences on the Knowledge Graph for Recommender Systems[C]// Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 2018: 417-426.
( Wu Xiyu, Chen Qimai, Liu Hai, et al. Collaborative Filtering Recommendation Algorithm Based on Representation Learning of Knowledge Graph[J]. Computer Engineering, 2018, 44(2):226-232, 263.)
( Hu Jiahui, Fang An, Zhao Wanqing, et al. Annotating Chinese E-Medical Record for Knowledge Discovery[J]. Data Analysis and Knowledge Discovery, 2019, 3(7):123-132.)
[10]
Liang H, Tsui B Y, Ni H, et al. Evaluation and Accurate Diagnoses of Pediatric Diseases Using Artificial Intelligence[J]. Nature Medicine, 2019, 25(3):433-438.
doi: 10.1038/s41591-018-0335-9
( Hou Mengwei, Wei Rong, Lu Liang, et al. Research Review of Knowledge Graph and Its Application in Medical Domain[J]. Journal of Computer Research and Development, 2018, 55(12):2587-2599.)
( Li Jian, Lu Peng, Tang Shihuan, et al. Analysis on Composition Principles for Lung Abscess by Using Traditional Chinese Medicine Inheritance System[J]. Chinese Journal of Experimental Traditional Medical Formulae, 2012, 18(2):254-257.)
( Yao Meicun, Ai Lu, Yuan Yuemei, et al. Analysis of the Association Rule in the Composition of the TCM Formulas for Diabetes[J]. Journal of Beijing University of TCM, 2002, 25(6):48-50.)
[14]
Shi Q, Zhao H, Chen J, et al. Study on TCM Syndrome Identification Modes of Coronary Heart Disease Based on Data Mining[J]. Evidence-Based Complementary and Alternative Medicine, 2012:Article ID 697028.
[15]
Yang H, Chen J, Tang S, et al. New Drug R&D of Traditional Chinese Medicine: Role of Data Mining Approaches[J]. Journal of Biological Systems, 2009, 17(3):329-347.
doi: 10.1142/S0218339009002971
[16]
Fang Y C, Huang H C, Chen H H, et al. TCMGeneDIT: A Database for Associated Traditional Chinese Medicine, Gene and Disease Information Using Text Mining[J]. BMC Complementary and Alternative Medicine, 2008, 8: Article No.58
( Gong Yanbing, Ni Qing, Wang Yongyan. Modern Methodology of TCM Syndrome Study(I): Data Mining Technology of TCM Syndrome[J]. Journal of Beijing University of TCM, 2006, 29(12):797-801.)
( Dai Xia, Guo Weixing. Survey of Research on Standardization of Diagnostic Criteria of TCM Syndromes[J]. Journal of Traditional Chinese Medicine, 2011, 52(2):168-171.)
[19]
Zhou X, Peng Y, Liu B. Text Mining for Traditional Chinese Medical Knowledge Discovery: A Survey[J]. Journal of Biomedical Informatics, 2010, 43(4):650-660.
doi: 10.1016/j.jbi.2010.01.002
( Shen Yi, Fu Ping, Kong Liya. Application of Data Mining Method in the Research of Famous and Old Traditional Chinese Medicine Drugs[J]. Journal of Traditional Chinese Medicine, 2016, 57(10):890-893.)
( Wang Huazhen, Peng Shujuan, Tiao Jin, et al. Data Visualization of Traditional Chinese Medicine Based on Random Forest[J]. Journal of System Simulation, 2014, 26(11):2751-2756.)
[22]
Lukman S, He Y, Hui S C. Computational Methods for Traditional Chinese Medicine: A Survey[J]. Computer Methods and Programs in Biomedicine, 2007, 88(3):283-294.
doi: 10.1016/j.cmpb.2007.09.008
[23]
Feng Y, Wu Z, Zhou X, et al. Knowledge Discovery in Traditional Chinese Medicine: State of the Art and Perspectives[J]. Artificial Intelligence in Medicine, 2006, 38(3):219-236.
pmid: 16930966
[24]
Zhou X, Chen S, Liu B, et al. Development of Traditional Chinese Medicine Clinical Data Warehouse for Medical Knowledge Discovery and Decision Support[J]. Artificial Intelligence in Medicine, 2010, 48(2-3):139-152.
doi: 10.1016/j.artmed.2009.07.012
[25]
Yang J, Tian S, Zhao J, et al. Exploring the Mechanism of TCM Formulae in the Treatment of Different Types of Coronary Heart Disease by Network Pharmacology and Machining Learning[J]. Pharmacological Research, 2020, 159:105034.
doi: 10.1016/j.phrs.2020.105034
[26]
Li Y H, Aslam M S, Yang K L, et al. Classification of Body Constitution Based on TCM Philosophy and Deep Learning[J]. Symmetry, 2020, 12(5):803.
doi: 10.3390/sym12050803
( Liu Yao, Sui Zhifang, Zhou Yang, et al. Research on Automatic Construction of Chinese Traditional Medicine Ontology Concept's Description Architecture[J]. New Technology of Library and Information Service, 2008(5):21-26.)
( Zhang Xiaohang, Shi Qinglei, Wang Bin, et al. Review of Machine Learning Algorithms in Traditional Chinese Medicine[J]. Computer Science, 2018, 45(S2):32-36.)
[29]
Zhang Z, Han X, Liu Z, et al. ERNIE: Enhanced Language Representation with Informative Entities[OL]. arXiv Preprint, arXiv: 1905.07129.
[30]
Agarwal R, Srikant R. Fast Algorithms for Mining Association Rules [C]//Proceedings of the 20th VLDB Conference. 1994, 487:499.
( Hu Shan, Liu Jing. Application of Fuzzy Comprehensive Evaluation Method in Product Design Scheme Decision[J]. Machinery Design, 2020, 37(1):135-139.)
[32]
Reimers N, Gurevych I. Sentence-Bert: Sentence Embeddings Using Siamese Bert-Networks[OL]. arXiv Preprint, arXiv: 1908.10084.