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.
Original language | English (US) |
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Pages (from-to) | 21-28 |
Number of pages | 8 |
Journal | Journal of Ethnopharmacology |
Volume | 109 |
Issue number | 1 |
DOIs | |
State | Published - Jan 3 2007 |
Keywords
- Herbal medicine
- Herbal prescriptions
- Herbal property
- Medicinal herb
- Statistical learning method
- Support vector machine
- TCM
- Traditional Chinese medicine
- Traditional medicines
ASJC Scopus subject areas
- Pharmacology
- Drug Discovery