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|Title:||Usefulness of traditionally defined herbal properties for distinguishing prescriptions of traditional Chinese medicine from non-prescription recipes||Authors:||Ung, C.Y.
Statistical learning method
Support vector machine
Traditional Chinese medicine
|Issue Date:||3-Jan-2007||Citation:||Ung, C.Y., Li, H., Kong, C.Y., Wang, J.F., Chen, Y.Z. (2007-01-03). Usefulness of traditionally defined herbal properties for distinguishing prescriptions of traditional Chinese medicine from non-prescription recipes. Journal of Ethnopharmacology 109 (1) : 21-28. ScholarBank@NUS Repository. https://doi.org/10.1016/j.jep.2006.06.007||Abstract:||Traditional Chinese medicine (TCM) has been widely practiced and is considered as an attractive to conventional medicine. Multi-herb recipes have been routinely used in TCM. These have been formulated by using TCM-defined herbal properties (TCM-HPs), the scientific basis of which is unclear. The usefulness of TCM-HPs was evaluated by analyzing the distribution pattern of TCM-HPs of the constituent herbs in 1161 classical TCM prescriptions, which shows patterns of multi-herb correlation. Two artificial intelligence (AI) methods were used to examine whether TCM-HPs are capable of distinguishing TCM prescriptions from non-TCM recipes. Two AI systems were trained and tested by using 1161 TCM prescriptions, 11,202 non-TCM recipes, and two separate evaluation methods. These systems correctly classified 83.1-97.3% of the TCM prescriptions, 90.8-92.3% of the non-TCM recipes. These results suggest that TCM-HPs are capable of separating TCM prescriptions from non-TCM recipes, which are useful for formulating TCM prescriptions and consistent with the expected correlation between TCM-HPs and the physicochemical properties of herbal ingredients responsible for producing the collective pharmacological and other effects of specific TCM prescriptions. © 2006 Elsevier Ireland Ltd. All rights reserved.||Source Title:||Journal of Ethnopharmacology||URI:||http://scholarbank.nus.edu.sg/handle/10635/106499||ISSN:||03788741||DOI:||10.1016/j.jep.2006.06.007|
|Appears in Collections:||Staff Publications|
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