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

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

Research output: Contribution to journalArticle

16 Citations (Scopus)

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
Externally publishedYes

Fingerprint

Chinese Traditional Medicine
Prescriptions
Medicine
Artificial Intelligence

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

Cite this

Usefulness of traditionally defined herbal properties for distinguishing prescriptions of traditional Chinese medicine from non-prescription recipes. / Ung, C. Y.; Li, Hu; Kong, C. Y.; Wang, J. F.; Chen, Y. Z.

In: Journal of Ethnopharmacology, Vol. 109, No. 1, 03.01.2007, p. 21-28.

Research output: Contribution to journalArticle

@article{9fd377163b4e4933b9281603161a7e5d,
title = "Usefulness of traditionally defined herbal properties for distinguishing prescriptions of traditional Chinese medicine from non-prescription recipes",
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.",
keywords = "Herbal medicine, Herbal prescriptions, Herbal property, Medicinal herb, Statistical learning method, Support vector machine, TCM, Traditional Chinese medicine, Traditional medicines",
author = "Ung, {C. Y.} and Hu Li and Kong, {C. Y.} and Wang, {J. F.} and Chen, {Y. Z.}",
year = "2007",
month = "1",
day = "3",
doi = "10.1016/j.jep.2006.06.007",
language = "English (US)",
volume = "109",
pages = "21--28",
journal = "Journal of Ethnopharmacology",
issn = "0378-8741",
publisher = "Elsevier Ireland Ltd",
number = "1",

}

TY - JOUR

T1 - Usefulness of traditionally defined herbal properties for distinguishing prescriptions of traditional Chinese medicine from non-prescription recipes

AU - Ung, C. Y.

AU - Li, Hu

AU - Kong, C. Y.

AU - Wang, J. F.

AU - Chen, Y. Z.

PY - 2007/1/3

Y1 - 2007/1/3

N2 - 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.

AB - 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.

KW - Herbal medicine

KW - Herbal prescriptions

KW - Herbal property

KW - Medicinal herb

KW - Statistical learning method

KW - Support vector machine

KW - TCM

KW - Traditional Chinese medicine

KW - Traditional medicines

UR - http://www.scopus.com/inward/record.url?scp=33845516461&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33845516461&partnerID=8YFLogxK

U2 - 10.1016/j.jep.2006.06.007

DO - 10.1016/j.jep.2006.06.007

M3 - Article

C2 - 16884871

AN - SCOPUS:33845516461

VL - 109

SP - 21

EP - 28

JO - Journal of Ethnopharmacology

JF - Journal of Ethnopharmacology

SN - 0378-8741

IS - 1

ER -