A data-driven method for syndrome type identification and classification in traditional Chinese medicine
Nevin L. Zhang, Chen Fu, Teng Fei Liu, Bao Xin Chen, Kin Man Poon, Pei, Xian Chen, Yun Ling Zhang

TL;DR
This paper introduces a novel data-driven approach using latent class analysis and symptom co-occurrence patterns to classify TCM syndrome types from survey data, supported by a software tool, demonstrated on VMCI data.
Contribution
It presents a six-step method for syndrome classification in TCM, relaxing LCA assumptions and providing a practical software package for implementation.
Findings
Identified TCM syndrome types in VMCI patients
Quantified syndrome prevalence and symptom patterns
Developed Lantern software for syndrome classification
Abstract
Objective: The efficacy of traditional Chinese medicine (TCM) treatments for Western medicine (WM) diseases relies heavily on the proper classification of patients into TCM syndrome types. We develop a data-driven method for solving the classification problem, where syndrome types are identified and quantified based on patterns detected in unlabeled symptom survey data. Method: Latent class analysis (LCA) has been applied in WM research to solve a similar problem, i.e., to identify subtypes of a patient population in the absence of a gold standard. A widely known weakness of LCA is that it makes an unrealistically strong independence assumption. We relax the assumption by first detecting symptom co-occurrence patterns from survey data and use those patterns instead of the symptoms as features for LCA. Results: The result of the investigation is a six-step method: Data collection,…
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