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

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.

SUBMITTER: Ferreira AS 

PROVIDER: S-EPMC2806360 | biostudies-literature | 2009 Dec

REPOSITORIES: biostudies-literature