女性更年期综合征与中医学体质相关性研究

根据目前中医体质学研究和中西医对更年期女性生理病理研究的结果，为深入探讨影响更年期综合征的相关因素，尤其与更年期女性体质的关系，预防和控制更年期综合征及其他疾病的发生，改善更年期女性体质。笔者对河北省45—55岁女性作了问卷调查，具体方法和结果如下。

河北省女性更年期病理体质调研分析

随着人口老龄化社会的来临，预防和控制中老年妇女易患病的发生，维护和增进其身心健康具有重要意义。中医理论认为，更年期女性体质的变化特点是肾精气渐衰，冲任亏虚，天癸将竭，气血不足，阴阳平衡失调。为深入探讨更年期女性的体质变化规律，笔者对河北省45—55岁女性的中医学体质状况作了抽样调查。为作对照研究，同时调查了部分35-44岁女性。采用现场问卷调查的方法进行，并对调查结果作以分析。

Construction and optimization of traditional Chinese medicine constitution prediction models based on deep learning

Objective To cater to the demands for personalized health services from a deep learning per-spective by investigating the characteristics of traditional Chinese medicine(TCM)constitu-tion data and constructing models to explore new prediction methods.Methods Data from students at Chengdu University of Traditional Chinese Medicine were collected and organized according to the 24 solar terms from January 21,2020,to April 6,2022.The data were used to identify nine TCM constitutions,including balanced constitution,Qi deficiency constitution,Yang deficiency constitution,Yin deficiency constitution,phlegm dampness constitution,damp heat constitution,stagnant blood constitution,Qi stagnation constitution,and specific-inherited predisposition constitution.Deep learning algorithms were employed to construct multi-layer perceptron(MLP),long short-term memory(LSTM),and deep belief network(DBN)models for the prediction of TCM constitutions based on the nine constitution types.To optimize these TCM constitution prediction models,this study in-troduced the attention mechanism(AM),grey wolf optimizer(GWO),and particle swarm op-timization(PSO).The models’performance was evaluated before and after optimization us-ing the F1-score,accuracy,precision,and recall.Results The research analyzed a total of 31655 pieces of data.(i)Before optimization,the MLP model achieved more than 90%prediction accuracy for all constitution types except the balanced and Qi deficiency constitutions.The LSTM model's prediction accuracies exceeded 60%,indicating that their potential in TCM constitutional prediction may not have been fully realized due to the absence of pronounced temporal features in the data.Regarding the DBN model,the binary classification analysis showed that,apart from slightly underperforming in predicting the Qi deficiency constitution and damp heat constitution,with accuracies of 65%and 60%,respectively.The DBN model demonstrated considerable discriminative power for other constitution types,achieving prediction accuracy rates and area under the receiver op-erating characteristic(ROC)curve(AUC)values exceeding 70%and 0.78,respectively.This indicates that while the model possesses a certain level of constitutional differentiation abili-ty,it encounters limitations in processing specific constitutional features,leaving room for further improvement in its performance.For multi-class classification problem,the DBN model’s prediction accuracy rate fell short of 50%.(ii)After optimization,the LSTM model,enhanced with the AM,typically achieved a prediction accuracy rate above 75%,with lower performance for the Qi deficiency constitution,stagnant blood constitution,and Qi stagna-tion constitution.The GWO-optimized DBN model for multi-class classification showed an increased prediction accuracy rate of 56%,while the PSO-optimized model had a decreased accuracy rate to 37%.The GWO-PSO-DBN model,optimized with both algorithms,demon-strated an improved prediction accuracy rate of 54%.Conclusion This study constructed MLP,LSTM,and DBN models for predicting TCM consti-tution and improved them based on different optimisation algorithms.The results showed that the MLP model performs well,the LSTM and DBN models were effective in prediction but with certain limitations.This study also provided a new technology reference for the es-tablishment and optimisation strategies of TCM constitution prediction models,and a novel idea for the treatment of non-disease.

Constitution identification model in traditional Chinese medicine based on multiple features

Objective To construct a precise model for identifying traditional Chinese medicine(TCM)constitutions;thereby offering optimized guidance for clinical diagnosis and treatment plan-ning;and ultimately enhancing medical efficiency and treatment outcomes.Methods First;TCM full-body inspection data acquisition equipment was employed to col-lect full-body standing images of healthy people;from which the constitutions were labelled and defined in accordance with the Constitution in Chinese Medicine Questionnaire(CCMQ);and a dataset encompassing labelled constitutions was constructed.Second;heat-suppres-sion valve(HSV)color space and improved local binary patterns(LBP)algorithm were lever-aged for the extraction of features such as facial complexion and body shape.In addition;a dual-branch deep network was employed to collect deep features from the full-body standing images.Last;the random forest(RF)algorithm was utilized to learn the extracted multifea-tures;which were subsequently employed to establish a TCM constitution identification mod-el.Accuracy;precision;and F1 score were the three measures selected to assess the perfor-mance of the model.Results It was found that the accuracy;precision;and F1 score of the proposed model based on multifeatures for identifying TCM constitutions were 0.842;0.868;and 0.790;respectively.In comparison with the identification models that encompass a single feature;either a single facial complexion feature;a body shape feature;or deep features;the accuracy of the model that incorporating all the aforementioned features was elevated by 0.105;0.105;and 0.079;the precision increased by 0.164;0.164;and 0.211;and the F1 score rose by 0.071;0.071;and 0.084;respectively.Conclusion The research findings affirmed the viability of the proposed model;which incor-porated multifeatures;including the facial complexion feature;the body shape feature;and the deep feature.In addition;by employing the proposed model;the objectification and intel-ligence of identifying constitutions in TCM practices could be optimized.

Randomized control study in the influence of different training methods of Tai Chi on Chinese medicine constitution of international students with depression or depression tendency

Objective： To evaluate the effect of different training methods of Tai Chi on Chinese medicine constitution of international students with depression or depression tendency. Methods： Thirty-eight Africa international students were randomly divided into the physical exercise group who receiving the simple physical exercise of simplified 24-form Tai Chi （physical exercise group） and the breathing group who receiving both deep breathing method plus simple physical exercise （breathing group）. The average scores of Chinese medicine gentleness and various biased constitutional types in the two groups were compared. Results： The average score of thirty-eight students with biased constitution in both groups decreased significantly compared with that before training （P 〈 0.05）. Compared with that before training, the average scores of biased constitutional types in the breathing group, including Qi deficiency, Yang deficiency, phlegm-dampness and Qi stagnation, were significantly decreased （P 〈 0.05）. The average scores of Yang deficiency, blood stasis and Qi stagnation constitutional types of students in simple physical group after training were also significantly decreased after training （P 〈 0.05）. Moreover, the average scores of biased constitutional types, including Qi deficiency, Yang deficiency, Yin deficiency, phlegm and dampness, Qi-stagnation, in the breathing group were significantly lower than those of the simple exercise group, suggesting the superior therapeutic effect of breathing training method （P 〈 0.01）. Conclusion： When combines with the deep breathing method, Tai Chi training achieves better effect on improving the biased constitutional types which may be related to depression or depression tendency, including Qi deficiency, Yang deficiency, Yin deficiency, phlegm and dampness, Qi-stagnation.