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Verifying TCM Syndrome Hypothesis Based on Improved Latent Tree Model

  • Nian Zhou
  • Lingshan Zhou
  • Lili Peng
  • Bing Wang
  • Peng Chen
  • Jun Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10955)

Abstract

Traditional Chinese Medicine (TCM) is a significant channel for the prevention and treatment of Chinese diseases and is increasingly popular among non-Chinese people. However, it suffered serious credibility problems. The fundamental question is that TCM syndrome differentiation is it a totally subjective question or is it based on evidence? In recent years, a method called latent tree analysis (LTA) has been put forward. The main idea is, based on statistical principles for cluster analysis of the epidemiological survey symptoms data, to discover latent variables implicated in the data and compare them with TCM syndromes. However, LTA has its own limitations. It states that one manifest variable in the latent tree model (LTM) can only correspond to one latent variable. This is inconsistent with the theory of traditional Chinese medicine. Therefore, this paper proposed an improved LTA, based on the LTM obtained from the original LTA, adding arrows between symptoms and syndromes. The current analysis used the improved LTA to study a dataset of 37,624 patients with hepatopathy. The latent variables found here well match the latent factors of TCM, in addition, there are also some symptoms associated with multiple syndromes, it not only provides evidence for the validity of the relevant TCM hypothesis in the case of hepatopathy and helps to classify these patients into TCM syndromes, but also proved that the improved LTM has a higher degree fitting to the original data.

Keywords

Latent tree model Syndrome differentiation TCM 

References

  1. 1.
    Normile, D.: The new face of traditional Chinese medicine. Science 299(5604), 188–190 (2003)CrossRefGoogle Scholar
  2. 2.
    World Health Organization: WHO International Standard Terminologies on Traditional Medicine in the Western Pacific Region. WHO Regional Office for the Western Pacific, Manila (2007)Google Scholar
  3. 3.
    Wu, D.X., Li, D.X., Yan, S.Y.: Fundamental Theories of Traditional Chinese Medicine. Science and Technology Press, Shanghai (1994)Google Scholar
  4. 4.
    Zhu, B., Wang, H.: Diagnostics of Traditional Chinese Medicine. Singing Dragon, London (2011)Google Scholar
  5. 5.
    Wang, H., Xu, Y.: The Current State and Future of Basic Theoretical Research on Traditional Chinese Medicine. Military Medical Sciences Press, Beijing (1999)Google Scholar
  6. 6.
    Feng, Y., Wu, Z., Zhou, X., Zhou, Z., Fan, W.: Knowledge discovery in traditional Chinese medicine: state of the art and perspectives. Artif. Intell. Med. 38(3), 219–236 (2006)CrossRefGoogle Scholar
  7. 7.
    Liang, M., Liu, J., Hong, Z., Xu, Y.: Perplexity of TCM Syndrome Research and Countermeasures. People’s Health Press, Beijing (1998)Google Scholar
  8. 8.
    Wang, B., Shen, H., Fang, A., D.-s., H., Jiang, C., Zhang, J., et al.: A regression model for calculating the second dimension retention index in comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry. J. Chromatogr. A 1451, 127–134 (2016)CrossRefGoogle Scholar
  9. 9.
    Wang, B., Chen, P., Wang, P., Zhao, G., Zhang, X.: Radial basis function neural network ensemble for predicting protein-protein interaction sites in heterocomplexes. Protein Pept. Lett. 17(9), 1111–1116 (2010)CrossRefGoogle Scholar
  10. 10.
    Zhang, N.L., Yuan, S., Chen, T., Wang, Y.: Latent tree models and diagnosis in traditional Chinese medicine. Artif. Intell. Med. 42(3), 229–245 (2008)CrossRefGoogle Scholar
  11. 11.
    Zhang, N.L., Yuan, S., Chen, T., Wang, Y.: Statistical validation of traditional Chinese medicine theories. J. Altern. Complement. Med. 14(5), 583–587 (2008)CrossRefGoogle Scholar
  12. 12.
    Zhang, N.L.: Hierarchical latent class models for cluster analysis. J. Mach. Learn. Res. 5(6), 697–723 (2004)MathSciNetzbMATHGoogle Scholar
  13. 13.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Elsevier, New York (2014)zbMATHGoogle Scholar
  14. 14.
    Chen, P., Hu, S., Zhang, J., Gao, X., Li, J., Xia, J., et al.: A sequence-based dynamic ensemble learning system for protein ligand-binding site prediction. IEEE/ACM Trans. Comput. Biol. Bioinform. (TCBB) 13(5), 901–912 (2016)CrossRefGoogle Scholar
  15. 15.
    Xia, S., Chen, P., Zhang, J., Li, X., Wang, B.: Utilization of rotation-invariant uniform LBP histogram distribution and statistics of connected regions in automatic image annotation based on multi-label learning. Neurocomputing 228, 11–18 (2017)CrossRefGoogle Scholar
  16. 16.
    Schwarz, G.: Estimating the dimension of a model. Ann. Stat. 6(2), 461–464 (1978)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Akaike, H.: A new look at the statistical model identification. IEEE Trans. Autom. Control 19(6), 716–723 (1974)MathSciNetCrossRefGoogle Scholar
  18. 18.
    ACSR: Bayesian classification (autoclass): theory and results (1996)Google Scholar
  19. 19.
    Cowell, R.G., Dawid, P., Lauritzen, S.L., Spiegelhalter, D.J.: Probabilistic Networks and Expert Systems: Exact Computational Methods for Bayesian Networks. Springer, New York (2006).  https://doi.org/10.1007/b97670CrossRefzbMATHGoogle Scholar
  20. 20.
    Zhang, N.L., Kocka, T., (eds.): Efficient learning of hierarchical latent class models. In: 16th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2004. IEEE (2004)Google Scholar
  21. 21.
    Chen, T., Zhang, N.L., Liu, T., Poon, K.M., Wang, Y.: Model-based multidimensional clustering of categorical data. Artif. Intell. 176(1), 2246–2269 (2012)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Zhang, N.L., Yi, W., Tao, C.: Discovery of latent structures: experience with the CoIL challenge 2000 data set. J. Syst. Sci. Complex. 21(2), 172–183 (2008)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Supervision CSBoT: National Standards on Clinic Terminology of Traditional Chinese Medicinal Diagnosis and Treatment—Syndromes. China Standards Press, Beijing (1997)Google Scholar
  24. 24.
    Yang, W.M.F., Jiang, Y.: Diagnostics of Traditional Chinese Medicine. Academy Press, Beijing (1998)Google Scholar
  25. 25.
    Yan, S.L., Zhang, L.W., Wang, M.H., Yuan, S.H.: Operational standards for determining the severity levels of kidney deficiency symptoms. J. Chengdu Univ. Chin. Med. 24(1), 56–59 (2001)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Nian Zhou
    • 1
    • 2
    • 3
  • Lingshan Zhou
    • 4
  • Lili Peng
    • 1
    • 2
    • 3
  • Bing Wang
    • 1
    • 2
    • 3
  • Peng Chen
    • 5
  • Jun Zhang
    • 6
  1. 1.School of Electronics and Information EngineeringTongji UniversityShanghaiChina
  2. 2.The Advanced Research Institute of Intelligent Sensing NetworkTongji UniversityShanghaiChina
  3. 3.The Key Laboratory of Embedded System and Service ComputingTongji UniversityShanghaiChina
  4. 4.Neurology DepartmentJinzhou Medical UniversityShenyangChina
  5. 5.Institute of Physical Science and Information TechnologyAnhui UniversityHefeiChina
  6. 6.School of Electronic Engineering and AutomationAnhui UniversityHefeiChina

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