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Evidence-Based Complementary and Alternative Medicine - Volume 2015 2015, Article ID 435085, 14 pages -

Research Article

School of Computer Science and Engineering, BeiHang University, Beijing, China

School of Information and Technologies, University of Sydney, Sydney, NSW, Australia

Shanghai University of Traditional Chinese Medicine, Shanghai, China

RMIT University, Melbourne, VIC, Australia

Received 30 October 2014; Revised 26 January 2015; Accepted 27 January 2015

Academic Editor: Kenji Watanabe

Copyright © 2015 Jinpeng Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Unlike the western medical approach where adrug is prescribed against specific symptoms of patients,traditional Chinese medicine TCM treatment has a uniquestep, which is called syndrome differentiation SD. It is arguedthat SD is considered as patient classification because priorto the selection of the most appropriate formula from a setof relevant formulae for personalization, a practitioner hasto label a patient belonging to a particular class syndromefirst. Hence, to detect the patterns between herbs and symptomsvia syndrome is a challenging problem; finding thesepatterns can help prepare a prescription that contributes tothe efficacy of a treatment. In order to highlight this uniquetriangular relationship of symptom, syndrome, and herb, wepropose a novel three-step mining approach. It first startswith the construction of a heterogeneous tripartite informationnetwork, which carries richer information. The second step isto systematically extract path-based topological features fromthis tripartite network. Finally, an unsupervised method is usedto learn the best parameters associated with different featuresin deciding the symptom-herb relationships. Experiments havebeen carried out on four real-world patient records Insomnia, Diabetes, Infertility, and Tourette syndrome with comprehensivemeasurements. Interesting and insightful experimental resultsare noted and discussed.

Autor: Jinpeng Chen, Josiah Poon, Simon K. Poon, Ling Xu, and Daniel M. Y. Sze



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