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Objective: To treat patients with vascular mild cognitive impairment (VMCI)using TCM, it is necessary to classify the patients into TCM syndrome types andto apply different treatments to different types. We investigate how toproperly carry out the classification using a novel data-driven method known aslatent tree analysis.  Method: A cross-sectional survey on VMCI was carried out in several regionsin northern China from 2008 to 2011, which resulted in a data set that involves803 patients and 93 symptoms. Latent tree analysis was performed on the data toreveal symptom co-occurrence patterns, and the patients were partitioned intoclusters in multiple ways based on the patterns. The patient clusters werematched up with syndrome types, and population statistics of the clusters areused 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 ofeach syndrome type are determined. Quantitative classification rules areestablished for determining whether a patient belongs to each of the syndrometypes.  Conclusions: A solution for the TCM syndrome classification problemassociated with VMCI is established based on the latent tree analysis ofunlabeled symptom survey data. The results can be used as a reference in clinicpractice to improve the quality of syndrome differentiation and to reducediagnosis variances across physicians. They can also be used for patientselection in research projects aimed at finding biomarkers for the syndrometypes and in randomized control trials aimed at determining the efficacy of TCMtreatments of VMCI.

Identification and classification of TCM syndrome types among patients with vascular mild cognitive impairment using latent tree analysis