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. 2011 Jan 12;6(1):1.
doi: 10.1186/1749-8546-6-1.

Misdiagnosis and undiagnosis due to pattern similarity in Chinese medicine: a stochastic simulation study using pattern differentiation algorithm

Arthur Sá Ferreira  1
Affiliations

Misdiagnosis and undiagnosis due to pattern similarity in Chinese medicine: a stochastic simulation study using pattern differentiation algorithm

Arthur Sá Ferreira. Chin Med. .

Abstract

Background: Whether pattern similarity causes misdiagnosis and undiagnosis in Chinese medicine is unknown. This study aims to test the effect of pattern similarity and examination methods on diagnostic outcomes of pattern differentiation algorithm (PDA).

Methods: A dataset with 73 Zangfu single patterns was used with manifestations according to the Four Examinations, namely inspection (Ip), auscultation and olfaction (AO), inquiry (Iq) and palpation (P). PDA was applied to 100 true positive and 100 true negative manifestation profiles per pattern in simulation. Four runs of simulations were used according to the Four Examinations: Ip, Ip+AO, Ip+AO+Iq and Ip+AO+Iq+P. Three pattern differentiation outcomes were separated, namely correct diagnosis, misdiagnosis and undiagnosis. Outcomes frequencies, dual pattern similarity and pattern-dataset similarity were calculated.

Results: Dual pattern similarity was associated with Four Examinations (gamma = -0.646, P < 0.01). Combination of Four Examinations was associated (gamma = -0.618, P < 0.01) with decreasing frequencies of pattern differentiation errors, being less influenced by pattern-dataset similarity (Ip: gamma = 0.684; Ip+AO: gamma = 0.660; Ip+AO+Iq: gamma = 0.398; Ip+AO+Iq+P: gamma = 0.286, P < 0.01 for all combinations).

Conclusion: Applied in an incremental manner, Four Examinations progressively reduce the association between pattern similarity and pattern differentiation outcome and are recommended to avoid misdiagnosis and undiagnosis due to similarity.

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Figures

Figure 1
Figure 1
Flowchart of the simulation study for investigation of pattern differentiation errors. Departing from Zangfu single patterns dataset, manifestation profiles were simulated according to the combination of examination methods. Cases (true positive) manifestation profiles were tested with criteria F%,K and N%-cutoff. Pattern differentiation outcomes (correct, misdiagnosis and undiagnosis) were categorized for analysis of association with pattern similarity and the Four Examinations.
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