
Construction of an Artificial Intelligence Traditional Chinese Medicine Diagnosis and Treatment Model Based on Syndrome Elements and Small-Sample Data
Jie Wang, Lian Duan, Hongzheng Li, Jinlei Liu, Hengwen Chen
Engineering ›› 2022, Vol. 8 ›› Issue (1) : 29-32.
Construction of an Artificial Intelligence Traditional Chinese Medicine Diagnosis and Treatment Model Based on Syndrome Elements and Small-Sample Data
[1] |
Zhang F, Wu C, Jia C, Gao K, Wang J, Zhao H, et al. Artificial intelligence based discovery of the association between depression and chronic fatigue syndrome. J Affect Disorders 2019;250:380–90.
|
[2] |
Wang Z, Li L, Song M, Yan J, Shi J, Yao Y. Evaluating the traditional Chinese medicine (TCM) officially recommended in China for COVID-19 using ontology-based side-effect prediction framework (OSPF) and deep learning. J Ethnopharmacol 2021;272:113957.
|
[3] |
Sun XL, Jiang WW, Yu DL, Lin X, Ding BG, Liu MA. Research on basic path and inherent law in the process of Chinese medicine treatment based on syndrome differentiation. J Tradit Chin Med 2016;57(4):289–94. Chinese.
|
[4] |
Wang YY. Suggestions on improving the system of traditional Chinese medicine syndrome differentiation. J Tradit Chin Med 2004;45(10):729–31. Chinese.
|
[5] |
Li S. Discussion on the characteristics of TCM syndromes from the concepts of dimension and ranks: approaches to the standardization of TCM syndromes. J Beijing University Tradit Chin Med 2003;26(3):1–4. Chinese.
|
[6] |
Zhang ZB, Wang YY. Establishment of a new TCM syndrome differentiation system. J Beijing University Tradit Chin Med 2005;28(1):1–3. Chinese.
|
[7] |
Zhang ZB, Wang YY. Research on TCM syndrome nomenclature and classification: review and hypothesis. J Beijing University Tradit Chin Med 2003;26(2):1–5. Chinese.
|
[8] |
Zhu WF. Standardization research of differentiation system of symptoms and signs and ‘‘syndrome” in TCM. Tianjin J Tradit Chin Med 2002;19(5):1–4. Chinese.
|
[9] |
Wang J, Tang YL, He QY, Xiong XJ. Thinking of prescriptions corresponding to syndromes in construction of syndrome differentiation system. J Tradit Chin Med 2009;24(7):837–9. Chinese.
|
[10] |
Wang J, Li J, Yao KW, Zhong JB. Study on syndrome elements and their combination laws in patients with angina pectoris. J Tradit Chin Med 2007;48 (10):920–2. Chinese.
|
[11] |
Li J, Wang J. Literature case analysis of 5099 cases of syndrome elements and syndrome combinations of angina pectoris of coronary heart disease. Chin J Basic Med Tradit Chin Med 2007;13(12):926–7. Chinese.
|
[12] |
China Association of Chinese Medicine. Guidelines for diagnosis and treatment of stable angina pectoris of coronary heart disease (T/CACM1325–2019). J Tradit Chin Med 2019;60(21):1880–90. Chinese.
|
[13] |
Zhang H, Ni W, Li J, Zhang J. Artificial intelligence-based traditional Chinese medicine assistive diagnostic system: validation study. JMIR Med Inform 2020;8(6):e17608.
|
[14] |
Xu F, Xu ZX, Xu WJ, Wang YW, Liu T, Xia CM, et al. Classification of TCM syndromes in 835 cases of coronary heart disease: on the basis of Bayesian networks principle. Shanghai J Tradit Chin Med 2014;48(1):10–3. Chinese.
|
[15] |
Zhang NL, Yuan S, Chen T, Wang Y. Statistical validation of traditional Chinese medicine theories. J Altern Complem Med 2008;14(5):583–7.
|
[16] |
Hong YZ, Zhou CL, Zhang ZF, Xu JT. Selection of characteristic symptoms of chronic fatigue syndrome elements based on random forest method. J Tradit Chin Med 2010;7:634–8. Chinese.
|
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|
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