Objective:To explore the feasibility of remotely obtaining complex information on traditional Chinese medicine(TCM)pulse conditions through voice signals.Methods: We used multi-label pulse conditions as the entry poin...Objective:To explore the feasibility of remotely obtaining complex information on traditional Chinese medicine(TCM)pulse conditions through voice signals.Methods: We used multi-label pulse conditions as the entry point and modeled and analyzed TCM pulse diagnosis by combining voice analysis and machine learning.Audio features were extracted from voice recordings in the TCM pulse condition dataset.The obtained features were combined with information from tongue and facial diagnoses.A multi-label pulse condition voice classification DNN model was built using 10-fold cross-validation,and the modeling methods were validated using publicly available datasets.Results: The analysis showed that the proposed method achieved an accuracy of 92.59%on the public dataset.The accuracies of the three single-label pulse manifestation models in the test set were 94.27%,96.35%,and 95.39%.The absolute accuracy of the multi-label model was 92.74%.Conclusion: Voice data analysis may serve as a remote adjunct to the TCM diagnostic method for pulse condition assessment.展开更多
Objective: To discuss the relationship between the postoperative breast cancer with distant metastasis and the TCM syndromes classification. Methods: 160 postoperative 5-year breast cancer patients from 1995 to 2000 w...Objective: To discuss the relationship between the postoperative breast cancer with distant metastasis and the TCM syndromes classification. Methods: 160 postoperative 5-year breast cancer patients from 1995 to 2000 were tracked, summed up and analysized TCM syndromes as stagnation of hepatic qi, deficiency of spleen and pathogenic phlegm reten- tion, blood stasis and toxin stagnation, deficiencies of both blood and qi. Results: (1) For blood stasis and toxin stagnation TCM syndrome, the metastatic rate raised to 45% during 5 years. However, the metastatic rates of other three TCM syn- dromes are 15%, 17.5% and 22.5% respectively. The general distant metastasis rate was 27.5% (P<0.01). (2) Lymph node metastasis, tumor size, Her-2 and its receptor have no obvious relation with TCM syndromes classification (P>0.05). Conclu- sion: (1) TCM syndrome classification has close relation with breast cancer distant metastasis. Distant metastasis have close relationship with blood stasis and toxin stagnation syndrome. (2) Lymph node metastasis, tumor size, Her-2 and its receptor have no obvious relation with TCM syndromes classification, which suggested that metastatic ability has been programmed in the early stage of carcinoma initiation. (3) Significantly enlightening for predict the prognosis under the guide of TCM syn- drome classification and take right therapeutic strategy: attack pathogen and activate blood circulation against cancer.展开更多
The Traditional Chinese Medicine (TCM) ConstitutionClassification and the Constitutions in Chinese MedicineQuestionnaire (CCMQ) are based on nearly 30 years' researchon physique structure,physiological functions,p...The Traditional Chinese Medicine (TCM) ConstitutionClassification and the Constitutions in Chinese MedicineQuestionnaire (CCMQ) are based on nearly 30 years' researchon physique structure,physiological functions,psychologicalcharacteristics and reactive states.Epidemiology,immunology,molecular biology,genetics,mathematical statistics and multicrosseddisciplines were applied and many arguments were putforward by TCM experts,epidemiological specialists and constitutionexperts to establish the standardization tool Classificationand Diagnosis Standards for the Constitutions of TCM.At thesame time,basic research on constitution classification wasconducted to supply the objective basis of the classification standards.The Standards were used to conduct 21,948 cases ofepidemiological investigation on a national scale and showedgreat applicability,practicability and maneuverability.TheStandards were applied abroad in the medical services of TCMand were also an effective tool in the development of preventivetreatment of diseases by TCM.展开更多
中医(traditional Chinese medicine, TCM)舌诊客观化研究中需要分析的舌象特征很多,不同的舌象特征往往采用单独的方法进行分析,导致分析系统的整体实现复杂度大幅增加。为此,基于持续学习的思想,提出一种中医舌色苔色协同分类方法,该...中医(traditional Chinese medicine, TCM)舌诊客观化研究中需要分析的舌象特征很多,不同的舌象特征往往采用单独的方法进行分析,导致分析系统的整体实现复杂度大幅增加。为此,基于持续学习的思想,提出一种中医舌色苔色协同分类方法,该方法将舌色分类作为旧任务,将苔色分类作为新任务,充分利用2个任务的相似性和相关性,仅通过一个网络结构就同时实现舌色和苔色的准确分类。首先,设计一种基于全局-局部混合注意力机制(global local hybrid attention, GLHA)的双分支网络结构,将网络高层语义特征与低层特征相融合,提升特征的表达能力;然后,提出基于正则化和回放相结合的持续学习策略,使得该网络在学习新任务知识的同时有效防止对旧任务知识的遗忘。在2个自建的中医舌象特征分析数据集上的实验结果表明,提出的协同分类方法可以获得与单个任务相当的分类性能,同时可以将2个分类任务的整体复杂度降低一半左右。其中,舌色分类准确率分别达到93.92%和92.97%,精确率分别达到93.69%和92.87%,召回率分别达到93.96%和93.16%;苔色分类准确率分别达到90.17%和90.26%,精确率分别达到90.05%和90.17%,召回率分别达到90.24%和90.29%。展开更多
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 ...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.展开更多
基金supported by Fundamental Research Funds from the Beijing University of Chinese Medicine(2023-JYB-KYPT-13)the Developmental Fund of Beijing University of Chinese Medicine(2020-ZXFZJJ-083).
文摘Objective:To explore the feasibility of remotely obtaining complex information on traditional Chinese medicine(TCM)pulse conditions through voice signals.Methods: We used multi-label pulse conditions as the entry point and modeled and analyzed TCM pulse diagnosis by combining voice analysis and machine learning.Audio features were extracted from voice recordings in the TCM pulse condition dataset.The obtained features were combined with information from tongue and facial diagnoses.A multi-label pulse condition voice classification DNN model was built using 10-fold cross-validation,and the modeling methods were validated using publicly available datasets.Results: The analysis showed that the proposed method achieved an accuracy of 92.59%on the public dataset.The accuracies of the three single-label pulse manifestation models in the test set were 94.27%,96.35%,and 95.39%.The absolute accuracy of the multi-label model was 92.74%.Conclusion: Voice data analysis may serve as a remote adjunct to the TCM diagnostic method for pulse condition assessment.
文摘Objective: To discuss the relationship between the postoperative breast cancer with distant metastasis and the TCM syndromes classification. Methods: 160 postoperative 5-year breast cancer patients from 1995 to 2000 were tracked, summed up and analysized TCM syndromes as stagnation of hepatic qi, deficiency of spleen and pathogenic phlegm reten- tion, blood stasis and toxin stagnation, deficiencies of both blood and qi. Results: (1) For blood stasis and toxin stagnation TCM syndrome, the metastatic rate raised to 45% during 5 years. However, the metastatic rates of other three TCM syn- dromes are 15%, 17.5% and 22.5% respectively. The general distant metastasis rate was 27.5% (P<0.01). (2) Lymph node metastasis, tumor size, Her-2 and its receptor have no obvious relation with TCM syndromes classification (P>0.05). Conclu- sion: (1) TCM syndrome classification has close relation with breast cancer distant metastasis. Distant metastasis have close relationship with blood stasis and toxin stagnation syndrome. (2) Lymph node metastasis, tumor size, Her-2 and its receptor have no obvious relation with TCM syndromes classification, which suggested that metastatic ability has been programmed in the early stage of carcinoma initiation. (3) Significantly enlightening for predict the prognosis under the guide of TCM syn- drome classification and take right therapeutic strategy: attack pathogen and activate blood circulation against cancer.
基金Supported by National Basic Research Program of China (No.2005CB523501)
文摘The Traditional Chinese Medicine (TCM) ConstitutionClassification and the Constitutions in Chinese MedicineQuestionnaire (CCMQ) are based on nearly 30 years' researchon physique structure,physiological functions,psychologicalcharacteristics and reactive states.Epidemiology,immunology,molecular biology,genetics,mathematical statistics and multicrosseddisciplines were applied and many arguments were putforward by TCM experts,epidemiological specialists and constitutionexperts to establish the standardization tool Classificationand Diagnosis Standards for the Constitutions of TCM.At thesame time,basic research on constitution classification wasconducted to supply the objective basis of the classification standards.The Standards were used to conduct 21,948 cases ofepidemiological investigation on a national scale and showedgreat applicability,practicability and maneuverability.TheStandards were applied abroad in the medical services of TCMand were also an effective tool in the development of preventivetreatment of diseases by TCM.
文摘中医(traditional Chinese medicine, TCM)舌诊客观化研究中需要分析的舌象特征很多,不同的舌象特征往往采用单独的方法进行分析,导致分析系统的整体实现复杂度大幅增加。为此,基于持续学习的思想,提出一种中医舌色苔色协同分类方法,该方法将舌色分类作为旧任务,将苔色分类作为新任务,充分利用2个任务的相似性和相关性,仅通过一个网络结构就同时实现舌色和苔色的准确分类。首先,设计一种基于全局-局部混合注意力机制(global local hybrid attention, GLHA)的双分支网络结构,将网络高层语义特征与低层特征相融合,提升特征的表达能力;然后,提出基于正则化和回放相结合的持续学习策略,使得该网络在学习新任务知识的同时有效防止对旧任务知识的遗忘。在2个自建的中医舌象特征分析数据集上的实验结果表明,提出的协同分类方法可以获得与单个任务相当的分类性能,同时可以将2个分类任务的整体复杂度降低一半左右。其中,舌色分类准确率分别达到93.92%和92.97%,精确率分别达到93.69%和92.87%,召回率分别达到93.96%和93.16%;苔色分类准确率分别达到90.17%和90.26%,精确率分别达到90.05%和90.17%,召回率分别达到90.24%和90.29%。
基金National Natural Science Foundation of China(81904324)Sichuan Science and Technology Department Project(2022YFS0194).
文摘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.
