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Title: A computer method for validating traditional Chinese medicine herbal prescriptions
Authors: Wang, J.F.
Cai, C.Z. 
Kong, C.Y.
Cao, Z.W.
Chen, Y.Z. 
Keywords: Alternative medicine
Complementary medicine
Herbal medicine
Medicinal plant
Support vector machines
Traditional medicine
Issue Date: 2005
Citation: Wang, J.F., Cai, C.Z., Kong, C.Y., Cao, Z.W., Chen, Y.Z. (2005). A computer method for validating traditional Chinese medicine herbal prescriptions. American Journal of Chinese Medicine 33 (2) : 281-297. ScholarBank@NUS Repository.
Abstract: Traditional Chinese medicine (TCM) has been widely practiced and is considered as an alternative to conventional medicine. TCM herbal prescriptions contain a mixture of herbs that collectively exert therapeutic actions and modulating effects. Traditionally defined herbal properties, related to the pharmacodynamic, pharmacokinetic and toxicological, as well as physicochemical properties of their principal ingredients, have been used as the basis for formulating TCM multi-herb prescriptions. These properties are used in this work to develop a computer program for predicting whether a multi-herb recipe is a valid TCM prescription. This program is based on a statistical learning method, support vector machine (SVM), and it is trained by using 575 well-known TCM prescriptions and 1961 non-TCM recipes generated by random combination of TCM herbs. Testing results by using 72 well-known TCM prescriptions and 5039 non-TCM recipes showed that 73.6% of the TCM prescriptions and 99.9% of non-TCM recipes are correctly classified by this system. A further test by using 48 TCM prescriptions published in recent years found that 68.7% of these are correctly classified. These accuracies are comparable to those of SVM classification of other biological systems. Our study indicates the potential of SVM for facilitating the analysis of TCM prescriptions. © 2005 World Scientific Publishing Company.
Source Title: American Journal of Chinese Medicine
ISSN: 0192415X
DOI: 10.1142/S0192415X05002825
Appears in Collections:Staff Publications

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