Topic model for chinese medicine diagnosis and prescription regularities analysis: Case on diabetes

Chinese Journal of Integrative Medicine

Volumn 17, Issue 4, 2011, Pages 307-313

  Zhang, Xiao Ping ^a   Zhou, Xue Zhong ^a   Huang, Hou Kuan ^a   Feng, Qi ^a   Chen, Shi Bo ^b   Liu, Bao Yan ^b  

^a BEIJING JIAOTONG UNIVERSITY   (China)

^b INSTITUTE OF CHINESE MATERIA MEDICA   (China)

Author keywords

Author topic model; Chinese medicine; Dirichlet priori; Latent dirichlet allocation; Symptom herb diagnosis topic model

Indexed keywords

ARTICLE; CHINESE MEDICINE; CLINICAL FEATURE; CLINICAL PRACTICE; CLINICAL STUDY; COMORBIDITY; HEART DISEASE; HOSPITAL PATIENT; HUMAN; KIDNEY DISEASE; NON INSULIN DEPENDENT DIABETES MELLITUS; PRESCRIPTION; PRIORITY JOURNAL; SYMPTOM HERB DIAGNOSIS TOPIC;

DIABETES MELLITUS, TYPE 2; DIAGNOSIS, DIFFERENTIAL; HUMANS; MEDICINE, CHINESE TRADITIONAL; MODELS, THEORETICAL;

EID: 79960116311     PISSN: 16720415     EISSN: 19930402     Source Type: Journal    
DOI: 10.1007/s11655-011-0699-x     Document Type: Article

References (34)

1. Feng Y, Wu ZH, Zhou XZ, Zhou ZM, Fan WY. Knowledge discovery in traditional Chinese medicine: State of the art and perspectives. Artif Intell Med 2006;38:219-236. (Pubitemid 44739269)
2. Lukman S, He Y, Hui SC. Computational methods for traditional Chinese medicine: A survey. Comput Methods Programs Biomed 2007;88:283-294. (Pubitemid 350116302)
3. Zhou XZ, Liu BY, Wang YH, Zhang RS, Li P, Chen SB, et al. Building clinical data warehouse for traditional Chinese medicine knowledge discovery. In: Proceedings of the 2008 International Conference on BioMedical Engineering and informatics. Sanya, Hainan, China; 2008:31-36.
4. Mitchell TM. Machine learning and data mining. Commun ACM 1999;42(11):31-36. (Pubitemid 129576746)
5. Zhou XZ, Liu BY, Wu ZH, Feng Y. Integrative mining of traditional Chinese medicine literature and MEDLINE for functional gene networks. Artif Intell Med 2007; 41: 87-104. (Pubitemid 47534222)
6. Zhou XZ, Liu BY. Traditional Chinese medicine clinical data mining: Experiences and issues. In: AIBDM workshop of 13th PAKDD. Bangkok, Thailand; 2009:11-20.
7. Steyvers M, Griffiths T. Probabilistic topic models. In: Landauer T, McNamara D, Dennis S, Kintsch W, eds. The handbook of latent semantic analysis: A road to meaning. Hillsdale, New Jersey: Erlbaum; 2007:427-448.
8. Rosen-Zvi M, Griffiths T, Steyvers M, Smyth P. The author- topic model for authors and documents. In: Proceedings of the 20th conference on uncertainty in artificial intelligence. Virginia: AUAI Press; 2004:487-494.
9. Zhou XZ, Chen SB, Liu BY, Zhang RS, Wang YH, Li P, et al. Development of traditional Chinese medicine clinical data warehouse for medical knowledge discovery and decision support. Artif Intell Med 2010;48:139-152.
10. Blei DM, Ng AY. Jordan MI. Latent Dirichlet allocation. J Machine Learn Res 2003;3:993-1022.
11. McCallum A, Wang XR, Corrada-Emmanuel A. Topic and role discovery in social networks with experiments on enron and academic email. J Artif Intell Res 2007;30:249-272.
12. Wang XR, Mohanty N, McCallum A. Group and topic discovery from relations and their attributes. In: Weiss Y, Schölkopf B, and Platt J, eds. Advances in Neural Information Processing Systems 18. Cambridge, MA: MIT Press; 2006:1449-1456.
13. Erosheva E, Fienberg S, Lafferty J. Mixed-membership models of scientific publications. Proc Natl Acad Sci U S A 2004;101(Suppl 1):5220-5227. (Pubitemid 38469130)
14. Mimno D, McCallum A. Expertise modeling for matching papers with reviewers. In: 13th ACM SIGKDD international conference on knowledge discovery and data mining. California, USA; 2007:500-509. (Pubitemid 350229235)
15. Zhang XP, Zhou XZ, Huang HK, Feng Q, Chen SB. Multi- relational topic model for cm clinical knowledge discovery. In: AIBDM workshop of 13th PAKDD. Bangkok, Thailand; 2009:31-39.
16. Gao Z, Po L, Jiang W, Zhao X, Dong H. A novel computerized method based on support vector machine for tongue diagnosis. In: Proceedings of the 3th International IEEE Conference on signal-image technologies and internet-based system. Shanghai, China; 2007:849-854.
17. Zhang Q, Zhang WT, Wei JJ, Wang XB, Liu P. Combined use of factor analysis and cluster analysis in classification of traditional Chinese medical syndromes in patients with posthepatitic cirrhosis. J Chin Integr Med 2005;3:14-18.
18. Qin ZG, Mao ZY, Deng ZZ. The application of rough set in the Chinese medicine rheumatic arthritis diagnosis. Chin J Biomed Eng (Chin) 2001;20:357-363.
19. Wang XW, Qu HB, Liu P, Cheng YY. A self-learning expert system for diagnosis in traditional Chinese medicine. Expert Syst Appl 2004;26: 557-566.
20. Deerwester SC, Dumais ST, Landauer TK, Furnas GW, Harshman RA. Indexing by latent semantic analysis. J Am Soc Inf Sci 1990;41:391-407.
21. Hofmann T. Unsupervised learning by probabilistic latent semantic analysis. Machine Learn J 2001;42:177-196. (Pubitemid 32872403)
22. American Diabetes Association. Report of the expert committee on the diagnosis and classification of diabetes mellitus. Diabetes Care 1997;25:s5-s20.
23. Zhou XZ, Peng YH, Liu BY. Text mining for traditional Chinese medical knowledge discovery: A survey. J Biomed Inf 2010;43:650-660.
24. Pang B, Zhang D, Li N, Wang K. Computerized tongue diagnosis based on Bayesian networks. IEEE Trans Biomed Eng 2004;51:1803-1810. (Pubitemid 39274427)
25. Zhang NL, Yuan SH, Chen T, Wang Y. Latent tree models and diagnosis in traditional Chinese Medicine. Artif Intell Med 2008;42:229-245.
26. Wu ZH, Zhou XZ, Liu BY, Cheng J. Text mining for finding functional community of related genes using CM knowledge. In: Boulicaut JF, Esposito F, Giannotti F, et al, eds. Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases. Berlin: Springer-Verlag; 2004:459-470.
27. Li C, Tang CJ, Peng J, Hu JJ. NNF: An effective approach in medicine paring analysis of traditional Chinese medicine prescriptions. In: Zhou L, Ooi B, Meng X, eds. Proceedings of DASFAA 2005, lecture notes in computer science 3453. Berlin: Springer-Verlag; 2005:576-581. (Pubitemid 41274149)
28. Yao MC, Ai L, Yuan YM, Qiao YJ. Analysis of the association rule in the composition of the cm formulas for diabetes. J Beijing Univ Chin Med (Chin) 2002;25(6):48-50.
29. Zhou ZM, Wu ZH, Wang CS, Feng Y. Mining both associated and correlated patterns. In: Alexandrov VN, et al, eds. Proceedings of ICCS, lecture notes in computer science 3994. Berlin: Springer-Verlag; 2006:468-475. (Pubitemid 44152251)
30. Deng K, Liu DL, Gao S, Geng Z. Structural learning of graphical models and its applications to traditional Chinese medicine. In: Wang L, Jin Y, eds. Proceedings of FSKD, lecture notes in computer science 3614. Berlin: Springer- Verlag; 2005:362-367. (Pubitemid 41480104)
31. Zhou XZ, Liu BY, Wu ZH. Text mining for clinical Chinese herbal medical knowledge discovery. In: Hoffmann AG, Motoda H, Scheffer T, eds. Discovery science, lecture notes in computer science 3735. Berlin: Springer-Verlag; 2005:396-398.
32. Cao C, Wang H, Sui Y. Knowledge modeling and acquisition of traditional Chinese herbal drugs and formulae from text. Artifl Intell Med 2004;32:3-13. (Pubitemid 39193183)
33. Zimmet P. Globalization, coca-colonization and the chronic disease epidemic: Can the doomsday scenario be averted? J Int Med 2000;247:301-310. (Pubitemid 30180030)
34. Covington MB. Traditional Chinese medicine in the treatment of diabetes. Diabetes Spectrum 2001;14: 154-159.