Artificial Intelligence in Medicine
Volume 42, Issue 3 , Pages 229-245 , March 2008

Latent tree models and diagnosis in traditional Chinese medicine

  • Nevin L. Zhang

      Affiliations

    • Department of Computer Science & Engineering, The Hong Kong University of Science & Technology, Clear Water Bay Road, Kowloon, Hong Kong, China
    • Corresponding Author InformationCorresponding author. Tel.: +852 2358 7015; fax: +852 2358 1477.
  • ,
  • Shihong Yuan

      Affiliations

    • Department of Chinese Medicine Diagnostics, Beijing University of Traditional Chinese Medicine, 11 Beisanhuan Donglu, Beijing 100029, China
  • ,
  • Tao Chen

      Affiliations

    • Department of Computer Science & Engineering, The Hong Kong University of Science & Technology, Clear Water Bay Road, Kowloon, Hong Kong, China
  • ,
  • Yi Wang

      Affiliations

    • Department of Computer Science & Engineering, The Hong Kong University of Science & Technology, Clear Water Bay Road, Kowloon, Hong Kong, China

Received 13 January 2007 ,Revised 17 October 2007 ,Accepted 25 October 2007.

References 

  1. Sung JJY, Leung WK, Ching JYL, Lao L, Zhang G, Wu JCY, et al. Agreements among traditional Chinese medicine practitioners in the diagnosis and treatment of irritable bowel syndrome. Alimen Pharmacol Therapeut. 2004;20:1205–1210
  2. World Health Organization, WHO traditional medicine strategy. http://www.who.int/medicines/publications/traditionalpolicy/en/index.html; 2002–2005 [accessed: 14.10.07].
  3. Wang HX, Xu YL. The current state and future of basic theoretical research on traditional Chinese medicine. Beijing: Military Medical Sciences Press; 1999;
  4. Feng Y, Wu Z, Zhou X, Zhou Z, Fan W. Knowledge discovery in traditional Chinese medicine: state of the art and perspectives. Aritif Intell Med. 2006;38:219–236
  5. Liang MX, Liu J, Hong ZP, Xu YY. Perplexity of TCM syndrome research and countermeasures. Beijing: People’s Health Press; 1998;
  6. Yang W, Meng F, Jiang Y. Diagnostics of traditional Chinese medicine. Beijing: Academy Press; 1998;
  7. Lazarsfeld PF, Henry NW. Latent structure analysis. Boston: Houghton Mifflin; 1968;
  8. Bartholomew DJ, Knott M. Latent variable models and factor analysis, Kendall’s Library of Statistics 7. 2nd ed.. London: Arnold; 1999;
  9. Vermunt JK, Magidson J. Latent class cluster analysis. In:  Hagenaars JA,  McCutcheon AL editor. Advances in latent class analysis. Cambridge University Press; 2002;p. 89–106
  10. J. Uebersax, A practical guide to local dependence in latent class models, http://ourworld.compuserve.com/homepages/jsuebersax/condep.htm [accessed 14.10.07].
  11. Hagenaars JA. Latent structure models with direct effects between indicators: local dependence models. Sociol Meth Res. 1988;16:379–405
  12. Goodman LA. Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika. 1974;61:215–231
  13. Zhang NL. Hierarchical latent class models for cluster analysis. In:  Dechter R,  Kearns M,  Sutton AAAI R editor. Proceedings of 18th National Conference on Artificial Intelligence. Menlo Park: AAAI Press. 2002;p. 230–237
  14. Zhang NL. Hierarchical latent class models for cluster analysis. J Machine Learn Res. 2004;5(6):697–723
  15. Pearl J. Probabilistic reasoning in intelligent systems: Networks of plausible inference. Palo Alto: Morgan Kaufmann; 1988;
  16. Durbin R, Eddy S, Krogh A, Mitchison G. Biological sequence analysis: Probabilistic models of proteins and nucleic acids. Cambridge University Press; 1998;
  17. Green P. Penalized likelihood. Encyclopedia of Statistical Sciences, Update Volume 3. John Wiley & Sons; 1999;p. 578–86
  18. Geiger D, Heckerman D, Meek C. Asymptotic model selection for directed networks with hidden variables. In:  Horvitz E,  Jensen FV editor. Proceedings of the 12th Annual Conference on Uncertainty in Artificial Intelligence. Palo Alto: Morgan Kaufmann. 1996;p. 158–168
  19. Kocka T, Zhang NL. Dimension correction for hierarchical latent class models. In:  Breese J,  Koller D editor. Proceedings of the 18th Conference on Uncertainty in Artificial Intelligence (UAI-02). Morgan Kaufmann; 2002;p. 267–274
  20. Zhang NL, Kocka T. Efficient learning of hierarchical latent class models. In:  Khoshgoftaar TM editors. Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI-2004). Boca Raton, Florida, November. 2004;p. 585–593
  21. Schwarz G. Estimating the dimension of a model. Ann Stat. 1978;6(2):461–464
  22. Zhang NL, Kocka T. Effective dimensions of hierarchical latent class models. J Artif Intell Res. 2004;21:1–17
  23. Dempster AP, Laird NM, Rubin DB. Maximum likelihood from incomplete data via the EM algorithm. J Roy Stat Soc B. 1997;39:1–38
  24. Lauritzen SL. The EM algorithm for graphical association models with missing data. Comput Stat Data Anal. 1995;19:191–201
  25. Friedman N. Learning belief networks in the presence of missing values and hidden variables. In:  Fisher DH editors. Proceedings of the 14th International Conference on Machine Learning (ICML 1997). Palo Alto: Morgan Kaufmann. 1997;p. 125–133
  26. In:  van er Putten P,  van Someren M editor. CoIL Challenge 2000: The insurance company case. Amsterdam: Sentient Machine Research; 2002;
  27. Chickering DM. Learning equivalence classes of Bayesian-network structures. J Machine Learn Res. 2002;2:445–498
  28. China State Bureau of Technical Supervision, National standards on clinic terminology of traditional Chinese Medical diagnosis and treatment—Syndromes, GB/T 16751.2–1997. Beijing: China Standards Press; 1997.
  29. Zhu WF. Diagnostics of traditional Chinese medicine. Shanghai Science Press; 1995;
  30. Yan SL, Zhang LW, Wang MH, Yuan SH. Operational standards for determining the severity levels of kidney deficiency symptoms. J Chengdu Univ Chin Med. 2001;24(1):56–59

PII: S0933-3657(07)00144-3

doi: 10.1016/j.artmed.2007.10.004

Artificial Intelligence in Medicine
Volume 42, Issue 3 , Pages 229-245 , March 2008