Guangzhou University of Chinese Medicine, Guangzhou 510016, China.
Henan University of Chinese Medicine, Zhengzhou 450046, China.
Comput Math Methods Med. 2022 Aug 21;2022:6217186. doi: 10.1155/2022/6217186. eCollection 2022.
Syndrome elements are regarded as the smallest unit of syndrome differentiation, which is characterized by indivisibility and random combination. Therefore, it can well fit the goal of syndrome differentiation and unity.
Clinical physicochemical indicators are important references for disease diagnosis, but they are often not used too much in the process of TCM syndrome differentiation. In the era of intelligence, communicating TCM syndrome differentiation at the macro level with physiological and pathological processes at the micro level (i.e., these clinical physicochemical indicators) is an effective tool to realize intelligent medicine. Taking the collected relevant clinical physical and chemical indexes as the research object, on the basis of routine -test and nonparametric test, logistic regression model is used to mine the main syndrome elements, and neural network multilayer perceptron is used to predict the feature model.
Compared with non-blood stasis patients, there were significant differences in HGB, PLT, Pt, PTA, Na, TG, LDL, BNP, LVEDd, and EF in blood stasis patients. Taking blood stasis as the dependent variable and the above physical and chemical indexes with statistical significance ( < 0.05) as independent variables. Compared with non-qi depression patients, there were significant differences in atpp, TG, TC, LDL, LVESD, and FS in qi depression patients ( < 0.05). Taking Yin deficiency as dependent variable and the above physical and chemical indexes (Hgb, APTT, CKMB, LVEDd, and LVPW) with statistical significance ( < 0.05) as independent variables, binary logistic regression analysis was carried out.
The combination pattern of physical and chemical indexes obtained from the neural network model provides a clinical reference basis for identifying the syndrome elements of unstable angina pectoris complicated with anxiety, such as blood stasis, qi depression, Qi deficiency, yin deficiency, phlegm turbidity, and qi stagnation.
证候要素被认为是辨证的最小单元，其特点是不可分割和随机组合。因此，它能够很好地符合辨证统一的目标。
临床理化指标是疾病诊断的重要参考，但在中医辨证过程中往往使用不多。在智能时代，将中医宏观辨证与微观生理病理过程（即这些临床理化指标）进行沟通是实现智能医学的有效工具。以收集到的相关临床理化指标为研究对象，在常规检验和非参数检验的基础上，采用逻辑回归模型挖掘主要证候要素，采用神经网络多层感知器预测特征模型。
与非血瘀患者相比，血瘀患者的 HGB、PLT、Pt、PTA、Na、TG、LDL、BNP、LVEDd 和 EF 有显著差异。以血瘀为因变量，以上有统计学意义的理化指标（<0.05）为自变量。与非气虚患者相比，气虚患者的 atpp、TG、TC、LDL、LVESD 和 FS 有显著差异（<0.05）。以阴虚为因变量，以上有统计学意义的理化指标（Hgb、APTT、CKMB、LVEDd 和 LVPW）为自变量，进行二项逻辑回归分析。
神经网络模型得出的理化指标组合模式，为识别焦虑不稳定性心绞痛的血瘀、气虚、气阴两虚、痰浊、气滞等证候要素提供了临床参考依据。
