Knowledge modeling in traditional chinese medicine with fuzzy influence diagrams

International Journal of Innovative Computing, Information and Control

Volumn 4, Issue 8, 2008, Pages 2057-2067

  Kao, Han Ying ^a   Huang, Chia Hui ^b   Kao, Tai Chi ^c   Kao, Han Chung ^d  

^a NATIONAL DONG HWA UNIVERSITY   (Taiwan)

^b KAINAN UNIVERSITY   (Taiwan)

^c SHANGHAI UNIVERSITY OF TRADITIONAL CHINESE MEDICINE   (China)

^d TAIPEI CITY HOSPITAL   (Taiwan)

Author keywords

Fuzzy influence diagrams; Knowledge models; Possibility distributions; Traditional Chinese medicine

Indexed keywords

EID: 63649154821     PISSN: 13494198     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (20)

1. E. Castillo, J. M. Gutoerre and A. S. Hadi, Expert Systems and Probabilistic Network Models, Springer-Verlag Inc., New York, 1997.
2. J. M. Charnes and P. P. Shenoy, Multistage Monte Carlo method for solving influence diagrams using local computation, Management Science, vol.50, pp.405-418, 2004.
3. T. R. Chen, Pediatrics in Traditional Chinese Medicine (in Chinese), Cheng Chung Book Co., Taipei, Taiwan, 1987.
4. Y. Feng, Z. H. Wu, X. Z. Zhou, Z. M. Zhou and W. Y. Fan, Knowledge discovery in traditional Chinese medicine: State of the art and perspectives, Artificial Intelligence in Medicine, vol.38, pp.219-236, 2006.
5. D. P. Filev and R. R. Yager, Operations on fuzzy numbers via fuzzy reasoning, Fuzzy Sets and Systems, vol.91, pp.137-142, 1997.
6. F. V. Jensen, Bayesian Networks and Decision Graphs, Springer-Verlag, New York, 2001.
7. S. L. Ji, M. Z. Yu, G. S. Chen and K. R. Jan, Diagnostics in Traditional Chinese Medicine (in Chinese), Grand East Enterprise Ltd., 2003.
8. P. Jia and C. D. Liao, Chinese-English Pocket Dictionary of Traditional Chinese Medicine, People's Medical Publishing House, Beijing, 2003.
9. H. Y. Kao and H. L. Li, A diagnostic reasoning and optimal treatment model for Bacterial infections with fuzzy information, Computer Methods and Programs in Biomedicine, vol.77, pp.23-37, 2005.
10. M. Lopez-Diaz and L. J. Rodríguez-Muñiz, Influence diagrams with super value nodes involving imprecise information, European Journal of Operational Research, vol.179, pp.203-219, 2007.
11. R. E. Neapolitan, Learning Bayesian Networks, Pearson Prentice Hall, 2004.
12. B. Pang, D. Zhang, N. Li and K. Q. Wang, Computerized tongue diagnosis based on Bayesian networks, IEEE Transactions on Biomedical Engineering, vol.51, pp.1803-1810, 2004.
13. J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann Publishers, Inc., 1997.
14. L. J. Rodríguez-Mufñz, M. Lopez-Diaz and A. M. Gil, Solving influence diagrams with fuzzy chance and value nodes, European Journal of Operational Research, vol.167, pp.444-460, 2005.
15. Shen-nong limited, http://www.shen-nong.com.
16. J. A. Tatman and R. D. Shacher, Dynamic programming and influence diagrams, IEEE Transactions on Systems, Man and Cybernetics, vol.20, pp.365-379, 1990.
17. D. M. Titterington, G. D. Murray, L. S. Murray, A. M. Spiegelhalter, A. M. Skene, J. D. F. Habbema and G. J. Gelpke, Comparison of discrimination techniques applied to a complex data-set of headinjured patients (with discussion), Journal of the Royal Statistical Society, Series A, vol.144, pp.145-175, 1981.
18. N. Wiseman and Y. Feng, Introduction to English Terminology of Chinese Medicine, Ho-Chi Publishing Co., Taipei, Taiwan Zhang YM, 2003.
19. K. Yamada, Diagnosis under compound effects and multiple causes by means of the conditional causal possibility approach, Fuzzy Sets and Systems, vol.145, pp.183-212, 2004.
20. L. A. Zadeh, Fuzzy sets as a basis for a theory of possibility, Fuzzy Sets and Systems, vol.1, pp.3-28, 1978.