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

Mining Symptom-Herb Patterns from Patient Records Using Tripartite Graph

1School of Computer Science and Engineering, BeiHang University, Beijing, China
2School of Information and Technologies, University of Sydney, Sydney, NSW, Australia
3Shanghai University of Traditional Chinese Medicine, Shanghai, China
4RMIT 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 a drug is prescribed against specific symptoms of patients, traditional Chinese medicine (TCM) treatment has a unique step, which is called syndrome differentiation (SD). It is argued that SD is considered as patient classification because prior to the selection of the most appropriate formula from a set of relevant formulae for personalization, a practitioner has to label a patient belonging to a particular class (syndrome) first. Hence, to detect the patterns between herbs and symptoms via syndrome is a challenging problem; finding these patterns can help prepare a prescription that contributes to the efficacy of a treatment. In order to highlight this unique triangular relationship of symptom, syndrome, and herb, we propose a novel three-step mining approach. It first starts with the construction of a heterogeneous tripartite information network, which carries richer information. The second step is to systematically extract path-based topological features from this tripartite network. Finally, an unsupervised method is used to learn the best parameters associated with different features in deciding the symptom-herb relationships. Experiments have been carried out on four real-world patient records (Insomnia, Diabetes, Infertility, and Tourette syndrome) with comprehensive measurements. Interesting and insightful experimental results are noted and discussed.