  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, symptom co-occurrence
pattern discovery, pattern interpretation, syndrome identification, syndrome
type identification, and syndrome type classification. A software package
called Lantern is developed to support the application of the method. The
method is illustrated using a data set on Vascular Mild Cognitive Impairment
(VMCI).
  Conclusions: A data-driven method for TCM syndrome identification and
classification is presented. The method can be used to answer the following
questions about a Western medicine disease: What TCM syndrome types are there
among the patients with the disease? What is the prevalence of each syndrome
type? What are the statistical characteristics of each syndrome type in terms
of occurrence of symptoms? How can we determine the syndrome type(s) of a
patient?
