Department of Neurology, Dongfang Hospital, Beijing University of Chinese Medicine, Beijing 100078, China.
Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.
J Integr Med. 2017 May;15(3):186-200. doi: 10.1016/S2095-4964(17)60335-2.
To treat patients with vascular mild cognitive impairment (VMCI) using traditional Chinese medicine (TCM), it is necessary to classify the patients into TCM syndrome types and to apply different treatments to different types. In this paper, we investigate how to properly carry out the classification for patients with VMCI aged 50 or above using a novel data-driven method known as latent tree analysis (LTA).
A cross-sectional survey on VMCI was carried out in several regions in Northern China between February 2008 and February 2012 which resulted in a data set that involves 803 patients and 93 symptoms. LTA 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.
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.
A solution for the TCM syndrome classification problem for patients with VMCI and aged 50 or above is established based on the LTA 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.
采用中医中药治疗血管性轻度认知障碍（VMCI）患者，需要对患者进行中医证型分类，并针对不同证型采用不同的治疗方法。本研究旨在采用一种新的数据驱动方法——潜在树分析（LTA），对 50 岁及以上 VMCI 患者进行中医证型分类。
2008 年 2 月至 2012 年 2 月，在中国北方多个地区开展 VMCI 的横断面调查，共纳入 803 例患者和 93 个症状。采用 LTA 对数据进行分析，揭示症状共现模式，并根据模式将患者分为多个亚群。将患者亚群与证型匹配，并对亚群的人口统计学特征进行量化，以确定证型并建立分类规则。
共识别出 8 种证型：气虚、气滞、血虚、血瘀、痰浊、火热、阳虚和阴虚。确定了每种证型的患病率和症状发生特点。建立了用于确定患者属于每种证型的定量分类规则。
基于对未经标记的症状调查数据的 LTA，为 50 岁及以上 VMCI 患者的中医证型分类问题提供了一种解决方案。该结果可作为临床实践中提高辨证质量、减少医生间诊断差异的参考依据，也可用于寻找证型生物标志物的研究项目和中医治疗 VMCI 的随机对照试验中，用于患者选择。
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