title: Mining Symptom-Herb Patterns from Patient Records Using Tripartite Graph.
abstract: 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.
authors.affiliation: School of Computer Science and Engineering, BeiHang University, Beijing, China.
authors.forename: Jinpeng
authors.initials: J
authors.lastname: Chen
authors.affiliation: School of Information and Technologies, University of Sydney, Sydney, NSW, Australia.
authors.forename: Josiah
authors.initials: J
authors.lastname: Poon
authors.affiliation: School of Information and Technologies, University of Sydney, Sydney, NSW, Australia.
authors.forename: Simon K
authors.initials: SK
authors.lastname: Poon
authors.affiliation: Shanghai University of Traditional Chinese Medicine, Shanghai, China.
authors.forename: Ling
authors.initials: L
authors.lastname: Xu
authors.affiliation: RMIT University, Melbourne, VIC, Australia.
authors.forename: Daniel M Y
authors.initials: DM
authors.lastname: Sze