Misdiagnosis and Undiagnosis Due to Pattern Similarity in Chinese Medicine: A Stochastic Simulation Study Using Pattern Differentiation Algorithm
Arthur Sá Ferreira
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.
Copyright © 2011 Sá Ferreira. This is an open access article distributed under the
Creative Commons Attribution License
1. Diagnostic process in Western and Chinese medicines
2. Types and sources of errors in pattern differentiation process
3. Assessment of errors in pattern differentiation process
1. Pattern dataset
2. Manifestation profile simulation algorithm
3. Pattern differentiation algorithm
4. Reference standard
5. Assessment of pattern similarity and diagnostic outcomes for error analysis
6. Statistical analysis
1. Study flowchart and simulation quality
2. Four Examinations and dual pattern similarity: intrinsic similarity
3. Four Examinations and pattern differentiation outcome: types of errors
4. Effects of pattern-dataset similarity on pattern differentiation errors
1. Distinction of pattern differentiation errors: misdiagnosis and undiagnosis
2. Effect of pattern similarity on pattern differentiation errors
3. Pattern differentiation errors due to pattern similarity are minimized under Four Examinations
4. Perspective for reducing errors due to pattern similarity and consequences of undesirable outcomes in clinical practice
5. Methodological considerations
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