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.

Constitution Identification System for Traditional Chinese Medicine (TCM) Based on Correlation Between TCM Constitution and Physical Examination Indices

Objective Identification of one’s constitution based on a combination of features and back propagation neural network theory is needed in modern medicine and traditional Chinese medicine(TCM).We describe a method to identify one’s constitution based on TCM constitution classification and a physical index model.Methods We created a constitution identification system based on neural network using Visio Studio development tool.We report the initial implementation of the system,the accuracy of which was verified using actual data.Results We found a relatively strong correlation between TCM constitution and physical indicators.Conclusion Finally,our report describes a possible application of the proposed system.

Application of Community TCM Constitution Identification in Health Management of the Elderly

Objective:To study the effect of adding traditional Chinese medicine(TCM)constitution identification to the health management of the elderly in Shuangzhao street of Xianyang city.Methods:A total of 142 elderly people were selected from January 2019 to January 2021 in this community and were divided into two groups which consists of 71 participants each.In the reference group,where health management is done based on the current routine of the health management measures of the community;on the other hand,the subjects of the experimental group incorporatesTCM Constitution identification to their health management routine.During the course of the experiment,the level of health awareness,the scores of physical indicators and quality of life,and the subjects’satisfaction with health management were compared between the two groups.Results:According to the statistical analysis of the experimental results,the level of health awareness of experimental subjects was 98.59%,while that of the reference group was only 76.06%,the difference between the two groups was significant and P<0.05;Based on the scores of physical indicators and quality of life of the subjects,the experimental group had significant advantages over those in the reference group(P<0.05);Questionnaires were used to investigate the subjects’satisfaction with health management.The satisfaction of the experimental group was 95.83%,while the satisfaction of the reference group was 80.28%,with a significant difference(P<0.05).Conclusion:Constitution of TCM identification application in community health management measures for the elderly can not only effectively improve the community elderly’s health knowledgeandact as a good disease prevention measure,but also can obviously improve the elderly’sphysical index and the quality of life.Besides,it also help build harmonious relations among residents of a community,and is worth popularizing among communities.

Consistency between traditional Chinese medicine constitution-based classification and genetic classification

Background:We studied the consistency between two classification systems for categorizing patients:traditional Chinese medicine(TCM)constitution-based methods,versus genetic clustering.Genetic classification in constitutional identification was also evaluated.Methods:A TCM physician evaluated the constitution of each patient,according to four examinations(inspection,auscultation-olfaction,interrogation,and palpation).Those who met the criteria for Yang-deficient,Yin-deficient,and balanced constitutions were enrolled in the study.Peripheral blood samples were obtained from the participants,and peripheral blood mononuclear cells were separated from the samples within 2 hours.Total RNA extraction from the white blood cells was performed;and an Affymetrix HG-U133 Plus2.0 array was used to determine the peripheral blood gene expression profiles.The samples were classified using a support vector machine genetic classifier,and the“leave-one-out”method was used for validation.Results:The global gene expression profiles of 32 samples were grouped into three categories,and the samples in each of the gene categories corresponded with the three constitution categories.The three constitution types were distinguished using the genetic classifier with 165 genes.The accuracy of the prediction classification was greater than 95%using mathematical method.Conclusions:Participants with Yin-deficient,Yang-deficient,and balanced constitutions have varying physical characteristics and gene expression patterns.Additionally,the results from TCM constitution classification matched those obtained by genetic classification.Finally,our preliminary gene classifier distinguishes among Yin-deficient,Yang-deficient,and balanced constitutions,and provides a methodological basis for identifying the different constitutions.

Characterization of material mechanical properties using strain correlation method combined with virtual fields method

The isotropic and anisotropic behaviors are considered as the important formats of the constitutive behaviors,and can also be called the global properties.To improve the identification ability of virtual fields method(VFM)when the global properties are unknown,this paper proposes the strain correlation method(SCM)to determine the global properties before the parameter identification using the VFM.Firstly,the basic principle of SCM is described in detail.Then,the feasibility and accuracy of SCM are verified through the numerical experiments based on the three-point bending configuration and the real experiment of polymethyl methacrylate(PMMA).The influence of the additive Gaussian white noise,local errors in the strain fields,and missing data at the specimen edges on the characterization results are evaluated.The results show that the SCM has good noise immunity and lower accuracy requirements for the strain fields.As an application,the mechanical properties of Ti-6A1-4V alloys fabricated by selective laser melting(SLM)are characterized by the SCM.The results show that the alloys are isotropic,and the isotropic VFM is utilized to determine the mechanical parameters.By using the SCM,the accuracy of identification results can be improved for the isotropic or bidirectional reinforced orthotropic materials when using VFM.