Skip to main content
SpringerLink
Log in
Book cover

International Conference on Intelligent Computing

ICIC 2018: Intelligent Computing Theories and Application pp 460–469Cite as

Verifying TCM Syndrome Hypothesis Based on Improved Latent Tree Model

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNISA,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.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Normile, D.: The new face of traditional Chinese medicine. Science 299(5604), 188–190 (2003)

    Article  Google Scholar 

  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. Wu, D.X., Li, D.X., Yan, S.Y.: Fundamental Theories of Traditional Chinese Medicine. Science and Technology Press, Shanghai (1994)

    Google Scholar 

  4. Zhu, B., Wang, H.: Diagnostics of Traditional Chinese Medicine. Singing Dragon, London (2011)

    Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  12. Zhang, N.L.: Hierarchical latent class models for cluster analysis. J. Mach. Learn. Res. 5(6), 697–723 (2004)

    MathSciNet  MATH  Google Scholar 

  13. Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Elsevier, New York (2014)

    MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  16. Schwarz, G.: Estimating the dimension of a model. Ann. Stat. 6(2), 461–464 (1978)

    Article  MathSciNet  Google Scholar 

  17. Akaike, H.: A new look at the statistical model identification. IEEE Trans. Autom. Control 19(6), 716–723 (1974)

    Article  MathSciNet  Google Scholar 

  18. ACSR: Bayesian classification (autoclass): theory and results (1996)

    Google Scholar 

  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/b97670

    Book  MATH  Google Scholar 

  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. 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)

    Article  MathSciNet  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  23. Supervision CSBoT: National Standards on Clinic Terminology of Traditional Chinese Medicinal Diagnosis and Treatment—Syndromes. China Standards Press, Beijing (1997)

    Google Scholar 

  24. Yang, W.M.F., Jiang, Y.: Diagnostics of Traditional Chinese Medicine. Academy Press, Beijing (1998)

    Google Scholar 

  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 

Download references

Author information

Authors and Affiliations

  1. School of Electronics and Information Engineering, Tongji University, Shanghai, China

    Nian Zhou, Lili Peng & Bing Wang

  2. The Advanced Research Institute of Intelligent Sensing Network, Tongji University, Shanghai, China

    Nian Zhou, Lili Peng & Bing Wang

  3. The Key Laboratory of Embedded System and Service Computing, Tongji University, Shanghai, China

    Nian Zhou, Lili Peng & Bing Wang

  4. Neurology Department, Jinzhou Medical University, Shenyang, Liaoning, China

    Lingshan Zhou

  5. Institute of Physical Science and Information Technology, Anhui University, Hefei, 230601, China

    Peng Chen

  6. School of Electronic Engineering and Automation, Anhui University, Hefei, 230601, China

    Jun Zhang

Authors
  1. Nian Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lingshan Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lili Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bing Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Peng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bing Wang .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Wuhan University of Science and Technology, Wuhan City, China

    Xiao-Long Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhou, N., Zhou, L., Peng, L., Wang, B., Chen, P., Zhang, J. (2018). Verifying TCM Syndrome Hypothesis Based on Improved Latent Tree Model. In: Huang, DS., Jo, KH., Zhang, XL. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10955. Springer, Cham. https://doi.org/10.1007/978-3-319-95933-7_55

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-95933-7_55

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-95932-0

  • Online ISBN: 978-3-319-95933-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation