Research

Diagnostic accuracy of pattern differentiation algorithm based on Chinese medicine theory: a stochastic simulation study

Arthur Sá Ferreira^1,2

¹  Department of Rehabilitation Science, Centro Universitário Augusto Motta, Av. Paris 72, Bonsucesso, Rio de Janeiro, BR CEP 21041-020, Brazil

²  Department of Physical Therapy, Universidade Salgado de Oliveira, Rua Marechal Deodoro 263, Niterói, Rio de Janeiro, BR CEP 24030-060, Brazil

author email corresponding author email

Chinese Medicine 2009, 4:24doi:10.1186/1749-8546-4-24

Published:	21 December 2009

Abstract

Background

Clinical practice of Chinese medicine requires little information for differentiation of Zang-fu patterns. This study is to test the impact of information amount on the diagnostic accuracy of pattern differentiation algorithm (PDA) using stochastic simulation of cases.

Methods

A dataset with 69 Zang-fu single patterns was used with manifestations according to the Four Examinations, namely inspection (Ip), auscultation and olfaction (AO), inquiry (Iq) and palpation (P). A variable quantity of available information (N_%) was randomly sampled to generate 100 true positive and 100 true negative manifestation profiles per pattern to which PDA was applied. Four runs of simulations were used according to the Four Examinations: Ip, Ip+AO, Ip+AO+Iq and Ip+AO+Iq+P. The algorithm performed pattern differentiation by ranking a list of diagnostic hypotheses by the amount of explained information F_%. Accuracy, sensitivity, specificity and negative and positive predictive values were calculated.

Results

Use the Four Examinations resulted in the best accuracy with the smallest cutoff value (N_% = 28.5%), followed by Ip+AO+Iq (33.5%), Ip+AO (51.5%) and Ip (52.0%). All tested combinations provided concave-shaped curves for accuracy, indicating an optimal value subject to N_%-cutoff. Use of N_%-cutoff as a secondary criterion resulted in 94.7% (94.3; 95.1) accuracy, 89.8% (89.1; 90.6) sensitivity, and 99.5% (99.3; 99.7) specificity with the Four Examinations.

Conclusion

Pattern differentiation based on both explained and optimum available information (F_% and N_%-cutoff) is more accurate than using explained and available information without cutoff (F_% and N_%). Both F_% and N_%-cutoff should be used as PDA's objective criteria to perform Zang-fu single pattern differentiation.