Tags

Type your tag names separated by a space and hit enter

A computer method for validating traditional Chinese medicine herbal prescriptions.
Am J Chin Med 2005; 33(2):281-97AJ

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.

Authors+Show Affiliations

Department of Computational Science, National University of Singapore Blk SOCI, Level 7, 3 Science Drivf 2, Singapore.No affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article

Language

eng

PubMed ID

15974487

Citation

Wang, J F., et al. "A Computer Method for Validating Traditional Chinese Medicine Herbal Prescriptions." The American Journal of Chinese Medicine, vol. 33, no. 2, 2005, pp. 281-97.
Wang JF, Cai CZ, Kong CY, et al. A computer method for validating traditional Chinese medicine herbal prescriptions. Am J Chin Med. 2005;33(2):281-97.
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. The American Journal of Chinese Medicine, 33(2), pp. 281-97.
Wang JF, et al. A Computer Method for Validating Traditional Chinese Medicine Herbal Prescriptions. Am J Chin Med. 2005;33(2):281-97. PubMed PMID: 15974487.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - A computer method for validating traditional Chinese medicine herbal prescriptions. AU - Wang,J F, AU - Cai,C Z, AU - Kong,C Y, AU - Cao,Z W, AU - Chen,Y Z, PY - 2005/6/25/pubmed PY - 2005/11/8/medline PY - 2005/6/25/entrez SP - 281 EP - 97 JF - The American journal of Chinese medicine JO - Am. J. Chin. Med. VL - 33 IS - 2 N2 - 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. SN - 0192-415X UR - https://www.unboundmedicine.com/medline/citation/15974487/A_computer_method_for_validating_traditional_Chinese_medicine_herbal_prescriptions_ L2 - https://www.worldscientific.com/doi/full/10.1142/S0192415X05002825?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub=pubmed DB - PRIME DP - Unbound Medicine ER -