Identification and classification of TCM syndrome types among patients
  with vascular mild cognitive impairment using latent tree analysis
  Objective: To treat patients with vascular mild cognitive impairment (VMCI)
using TCM, it is necessary to classify the patients into TCM syndrome types and
to apply different treatments to different types. We investigate how to
properly carry out the classification using a novel data-driven method known as
latent tree analysis.
  Method: A cross-sectional survey on VMCI was carried out in several regions
in northern China from 2008 to 2011, which resulted in a data set that involves
803 patients and 93 symptoms. Latent tree analysis was performed on the data to
reveal symptom co-occurrence patterns, and the patients were partitioned into
clusters in multiple ways based on the patterns. The patient clusters were
matched up with syndrome types, and population statistics of the clusters are
used to quantify the syndrome types and to establish classification rules.
  Results: Eight syndrome types are identified: Qi Deficiency, Qi Stagnation,
Blood Deficiency, Blood Stasis, Phlegm-Dampness, Fire-Heat, Yang Deficiency,
and Yin Deficiency. The prevalence and symptom occurrence characteristics of
each syndrome type are determined. Quantitative classification rules are
established for determining whether a patient belongs to each of the syndrome
types.
  Conclusions: A solution for the TCM syndrome classification problem
associated with VMCI is established based on the latent tree analysis of
unlabeled symptom survey data. The results can be used as a reference in clinic
practice to improve the quality of syndrome differentiation and to reduce
diagnosis variances across physicians. They can also be used for patient
selection in research projects aimed at finding biomarkers for the syndrome
types and in randomized control trials aimed at determining the efficacy of TCM
treatments of VMCI.
