Visual agreement analyses of traditional chinese medicine: A multiple-dimensional scaling approach

Lun Chien Lo, John Y. Chiang, Tsung Lin Cheng, Pei Shuan Shieh

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

The study of TCM agreement in terms of a powerful statistical tool becomes critical in providing objective evaluations. Several previous studies have conducted on the issue of consistency of TCM, and the results have indicated that agreements are low. Traditional agreement measures only provide a single value which is not sufficient to justify if the agreement among several raters is strong or not. In light of this observation, a novel visual agreement analysis for TCM via multiple dimensional scaling (MDS) is proposed in this study. If there are clusters present in the raters in a latent manner, MDS can prove itself as an effective distinguisher. In this study, a group of doctors, consisting of 11 experienced TCM practitioners having clinical experience ranging from 3 to 15 years with a mean of 5.5 years from the Chinese Medicine Department at Changhua Christian Hospital (CCH) in Taiwan were asked to diagnose a total of fifteen tongue images, the Eight Principles derived from the TCM theorem. The results of statistical analysis show that, if there are clusters present in the raters in a latent manner, MDS can prove itself as an effective distinguisher.

Original languageEnglish
Article number516473
JournalEvidence-based Complementary and Alternative Medicine
Volume2012
DOIs
Publication statusPublished - 2012 Oct 29

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Chinese Traditional Medicine
Taiwan
Tongue
Cluster Analysis
Medicine

All Science Journal Classification (ASJC) codes

  • Complementary and alternative medicine

Cite this

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Visual agreement analyses of traditional chinese medicine : A multiple-dimensional scaling approach. / Lo, Lun Chien; Chiang, John Y.; Cheng, Tsung Lin; Shieh, Pei Shuan.

In: Evidence-based Complementary and Alternative Medicine, Vol. 2012, 516473, 29.10.2012.

Research output: Contribution to journalArticle

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