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Hsu, E. (2018). Traditional Chinese medicine: its philosophy, history, and practice. The International Encyclopedia of Anthropology, 1-10.HsuE. (2018). Traditional Chinese medicine: its philosophy, history, and practice. The International Encyclopedia of Anthropology, 1-10.Search in Google Scholar
Dou, Z., Xia, Y., Zhang, J., Li, Y., Zhang, Y., Zhao, L., ... & Liu, Y. (2021). Syndrome Differentiation and Treatment Regularity in Traditional Chinese Medicine for Type 2 Diabetes: A Text Mining Analysis. Frontiers in Endocrinology, 12, 728032-728032.DouZ.XiaY.ZhangJ.LiY.ZhangY.ZhaoL.LiuY. (2021). Syndrome Differentiation and Treatment Regularity in Traditional Chinese Medicine for Type 2 Diabetes: A Text Mining Analysis. Frontiers in Endocrinology, 12, 728032-728032.Search in Google Scholar
Tianqi, W. (2020). Retesting Conjecture of Traditional Chinese Medicine Theory. Annals of Clinical and Medical Case Reports, 5(6), 1-4.TianqiW. (2020). Retesting Conjecture of Traditional Chinese Medicine Theory. Annals of Clinical and Medical Case Reports, 5(6), 1-4.Search in Google Scholar
Huang, Y., & Zhu, L. (Eds.). (2024). Textbook of Traditional Chinese Medicine: Volume 1: Introduction, Examination, Etiologies and Pathogenesis and Differentiation of Syndromes (Vol. 1). Springer Nature.HuangY.ZhuL. (2024). Textbook of Traditional Chinese Medicine: Volume 1: Introduction, Examination, Etiologies and Pathogenesis and Differentiation of Syndromes. 1. Springer Nature.Search in Google Scholar
Jiang, T. T., & Li, J. C. (2020). Review on the systems biology research of Yin-deficiency-heat syndrome in traditional Chinese medicine. Anatomical Record (Hoboken, NJ: 2007).JiangT. T.LiJ. C. (2020). Review on the systems biology research of Yin-deficiency-heat syndrome in traditional Chinese medicine. Anatomical Record (Hoboken, NJ: 2007).Search in Google Scholar
Zhao, Y., Peng, H., Wang, S., & Liu, J. (2020). Clinical analysis of acute coronary syndrome patients with Qi-blood syndromes: establishment of a diagnostic prediction model for syndrome differentiation. Annals of Palliative Medicine, 9(4), 2096110-2092110.ZhaoY.PengH.WangS.LiuJ. (2020). Clinical analysis of acute coronary syndrome patients with Qi-blood syndromes: establishment of a diagnostic prediction model for syndrome differentiation. Annals of Palliative Medicine, 9(4), 2096110-2092110.Search in Google Scholar
Yang, X., & Wu, X. Z. (2017). The combination of disease and Zheng (syndrome) on the basic of differentiation of six channels: a new pattern of disease diagnosis and treatment of traditional Chinese medicine. Traditional Medicine Research, 2(2), 100.YangX.WuX. Z. (2017). The combination of disease and Zheng (syndrome) on the basic of differentiation of six channels: a new pattern of disease diagnosis and treatment of traditional Chinese medicine. Traditional Medicine Research, 2(2), 100.Search in Google Scholar
Leung, H. Y. C., Leong, P. K., Chen, J., & Ko, K. M. (2017). Inter-Organ Relationships among Gut, Lung and Skin beyond the Pathogenesis of Allergies: Relevance to the Zang-Fu Theory in Chinese Medicine. Chinese Medicine, 8, 73-81.LeungH. Y. C.LeongP. K.ChenJ.KoK. M. (2017). Inter-Organ Relationships among Gut, Lung and Skin beyond the Pathogenesis of Allergies: Relevance to the Zang-Fu Theory in Chinese Medicine. Chinese Medicine, 8, 73-81.Search in Google Scholar
Ahuja, R., Chug, A., Gupta, S., Ahuja, P., & Kohli, S. (2020). Classification and Clustering Algorithms of Machine Learning with their Applications. Nature-Inspired Computation in Data Mining and Machine Learning, 225.AhujaR.ChugA.GuptaS.AhujaP.KohliS. (2020). Classification and Clustering Algorithms of Machine Learning with their Applications. Nature-Inspired Computation in Data Mining and Machine Learning, 225.Search in Google Scholar
Kowsari, K., Jafari Meimandi, K., Heidarysafa, M., Mendu, S., Barnes, L., & Brown, D. (2019). Text Classification Algorithms: A Survey. Information (2078-2489), 10(4).KowsariK.Jafari MeimandiK.HeidarysafaM.MenduS.BarnesL.BrownD. (2019). Text Classification Algorithms: A Survey. Information (2078-2489), 10(4).Search in Google Scholar
Shrivastava, A., & Dubey, R. (2018, December). Classification of Spam Mail using different machine learning algorithms. In 2018 International Conference on Advanced Computation and Telecommunication (ICACAT) (pp. 1-10). IEEE.ShrivastavaA.DubeyR. (2018, December). Classification of Spam Mail using different machine learning algorithms. 2018 International Conference on Advanced Computation and Telecommunication (ICACAT), 1-10. IEEE.Search in Google Scholar
Portugal, I., Alencar, P., & Cowan, D. (2018). The use of machine learning algorithms in recommender systems: A systematic review. Expert Systems with Applications, 100(97), 205-227.PortugalI.AlencarP.CowanD. (2018). The use of machine learning algorithms in recommender systems: A systematic review. Expert Systems with Applications, 100(97), 205-227.Search in Google Scholar
Dang, Y., Jiang, N., Hu, H., Ji, Z., & Zhang, W. (2018). Image classification based on quantum K-Nearest-Neighbor algorithm. Quantum Information Processing, 17(9), 239.DangY.JiangN.HuH.JiZ.ZhangW. (2018). Image classification based on quantum K-Nearest-Neighbor algorithm. Quantum Information Processing, 17(9), 239.Search in Google Scholar
Chen, L., Li, S., Bai, Q., Yang, J., Jiang, S., & Miao, Y. (2021). Review of Image Classification Algorithms Based on Convolutional Neural Networks. Remote Sensing, 13(22).ChenL.LiS.BaiQ.YangJ.JiangS.MiaoY. (2021). Review of Image Classification Algorithms Based on Convolutional Neural Networks. Remote Sensing, 13(22).Search in Google Scholar
Chen, H., Songhua, H., Rui, H., & Xiuju, Z. (2021). Improved naive Bayes classification algorithm for traffic risk management. EURASIP Journal on Advances in Signal Processing, 2021(1).ChenH.SonghuaH.RuiH.XiujuZ. (2021). Improved naive Bayes classification algorithm for traffic risk management. EURASIP Journal on Advances in Signal Processing, 2021(1).Search in Google Scholar
Liu, Y., Bi, J. W., & Fan, Z. P. (2017). A method for multi-class sentiment classification based on an improved one-vs-one (OVO) strategy and the support vector machine (SVM) algorithm. Information Sciences–Informatics and Computer Science, Intelligent Systems, Applications: An International Journal, 394(C), 38-52.LiuY.BiJ. W.FanZ. P. (2017). A method for multi-class sentiment classification based on an improved one-vs-one (OVO) strategy and the support vector machine (SVM) algorithm. Information Sciences–Informatics and Computer Science, Intelligent Systems, Applications: An International Journal, 394(C), 38-52.Search in Google Scholar
de Caigny, A., Coussement, K., & de Bock, K. W. (2018). A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees. European Journal of Operational Research.de CaignyA.CoussementK.de BockK. W. (2018). A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees. European Journal of Operational Research.Search in Google Scholar
Li, X., Zhang, Y., Wang, Y., Xu, J., Xin, P., Meng, Y., ... & Kuang, H. (2017). The Mechanisms of Traditional Chinese Medicine Underlying the Prevention and Treatment of Parkinson’s Disease. Frontiers in Pharmacology, 8, 634-634.LiX.ZhangY.WangY.XuJ.XinP.MengY.KuangH. (2017). The Mechanisms of Traditional Chinese Medicine Underlying the Prevention and Treatment of Parkinson’s Disease. Frontiers in Pharmacology, 8, 634-634.Search in Google Scholar
Lyu, S., Zhang, C. S., Mao, Z., Guo, X., Li, Z., Luo, X., ... & Su, Q. (2024). Real-world Chinese herbal medicine for Parkinson’s disease: a hospital-based retrospective analysis of electronic medical records. Frontiers in Aging Neuroscience, 16, 1362948.LyuS.ZhangC. S.MaoZ.GuoX.LiZ.LuoX.SuQ. (2024). Real-world Chinese herbal medicine for Parkinson’s disease: a hospital-based retrospective analysis of electronic medical records. Frontiers in Aging Neuroscience, 16, 1362948.Search in Google Scholar
Peng, Y., Tang, C., Chen, G., Xie, J., & Wang, C. (2017, November). Multi-label learning by exploiting label correlations for TCM diagnosing Parkinson’s disease. In 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 590-594). IEEE Computer Society.PengY.TangC.ChenG.XieJ.WangC. (2017, November). Multi-label learning by exploiting label correlations for TCM diagnosing Parkinson’s disease. 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 590-594. IEEE Computer Society.Search in Google Scholar
Jun, P., Zhao, H., Jung, I. C., Kwon, O., Han, C. H., Won, J., & Jang, J. H. (2023). Efficacy of herbal medicine treatment based on syndrome differentiation for Parkinson’s disease: A systematic review and meta-analysis of randomized placebo-controlled clinical trials. Frontiers in Pharmacology, 14, 1108407.JunP.ZhaoH.JungI. C.KwonO.HanC. H.WonJ.JangJ. H. (2023). Efficacy of herbal medicine treatment based on syndrome differentiation for Parkinson’s disease: A systematic review and meta-analysis of randomized placebo-controlled clinical trials. Frontiers in Pharmacology, 14, 1108407.Search in Google Scholar
Xia, X., Dong, X., Li, K., Song, J., Tong, D., Liu, Y., ... & Wang, D. (2023). Treatment of Parkinson disease by acupuncture combined with medicine based on syndrome differentiation from the perspective of modern medicine: A review. Medicine, 102(30), e34278.XiaX.DongX.LiK.SongJ.TongD.LiuY.WangD. (2023). Treatment of Parkinson disease by acupuncture combined with medicine based on syndrome differentiation from the perspective of modern medicine: A review. Medicine, 102(30), e34278.Search in Google Scholar
Hu, Y., Gu, S., Yuan, X., Li, H., Yuan, C., & Ye, Q. (2022). Traditional Chinese medicine syndrome differentiation and treatment by stages of Parkinson’s disease: study protocol for a multicentre, randomized, double-blind, placebo-controlled clinical trial. Chinese Medicine, 17, 1.HuY.GuS.YuanX.LiH.YuanC.YeQ. (2022). Traditional Chinese medicine syndrome differentiation and treatment by stages of Parkinson’s disease: study protocol for a multicentre, randomized, double-blind, placebo-controlled clinical trial. Chinese Medicine, 17, 1.Search in Google Scholar
Jun, P., Zhao, H., Jung, I. C., Jang, E., Kwon, O., & Jang, J. H. (2024). Traditional Medicine Classification Based on the Nature and Location of Disease in Parkinson’s Disease: A Clustering Study Using Pattern Identification Disassemble Presented in Clinical Studies. Journal of Integrative and Complementary Medicine, 30(2), 99-106.JunP.ZhaoH.JungI. C.JangE.KwonO.JangJ. H. (2024). Traditional Medicine Classification Based on the Nature and Location of Disease in Parkinson’s Disease: A Clustering Study Using Pattern Identification Disassemble Presented in Clinical Studies. Journal of Integrative and Complementary Medicine, 30(2), 99-106.Search in Google Scholar
Zhao, H., Kwon, O., Cha, J., Jung, I. C., Jun, P., Jang, J. Y., & Jang, J. H. (2024). Exploring traditional medicine diagnostic classification for parkinson’s disease using hierarchical clustering. Complement Med Res, 1-15.ZhaoH.KwonO.ChaJ.JungI. C.JunP.JangJ. Y.JangJ. H. (2024). Exploring traditional medicine diagnostic classification for parkinson’s disease using hierarchical clustering. Complement Med Res, 1-15.Search in Google Scholar
Huang, Z., Miao, J., Chen, J., Zhong, Y., Yang, S., Ma, Y., & Wen, C. (2022). A Traditional Chinese Medicine Syndrome Classification Model Based on Cross-Feature Generation by Convolution Neural Network: Model Development and Validation. JMIR Medical Informatics, 10(4).HuangZ.MiaoJ.ChenJ.ZhongY.YangS.MaY.WenC. (2022). A Traditional Chinese Medicine Syndrome Classification Model Based on Cross-Feature Generation by Convolution Neural Network: Model Development and Validation. JMIR Medical Informatics, 10(4).Search in Google Scholar
Teng, S., Fu, A., Lu, W., & Li, Z. (2023). TCM syndrome classification using graph convolutional network. European Journal of Integrative Medicine, 62, 102288.TengS.FuA.LuW.LiZ. (2023). TCM syndrome classification using graph convolutional network. European Journal of Integrative Medicine, 62, 102288.Search in Google Scholar