Usefulness of traditionally defined herbal properties for distinguishing prescriptions of traditional Chinese medicine from non-prescription recipes

C. Y. Ung, H. Li, C. Y. Kong, J. F. Wang, Y. Z. Chen

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

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 languageEnglish (US)
Pages (from-to)21-28
Number of pages8
JournalJournal of Ethnopharmacology
Volume109
Issue number1
DOIs
StatePublished - 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

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