Abstract
OBJECTIVE: 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. METHODS: 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 t-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. RESULTS: 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 (P < 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 (P < 0.05). Taking Yin deficiency as dependent variable and the above physical and chemical indexes (Hgb, APTT, CKMB, LVEDd, and LVPW) with statistical significance (P < 0.05) as independent variables, binary logistic regression analysis was carried out. CONCLUSION: 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.