Traditional Chinese medicine(TCM)is an ancient medical system distinctive and effective in treating cancer,depression,coronavirus disease 2019(COVID-19),and other diseases.However,the relatively abstract diagnostic me...Traditional Chinese medicine(TCM)is an ancient medical system distinctive and effective in treating cancer,depression,coronavirus disease 2019(COVID-19),and other diseases.However,the relatively abstract diagnostic methods of TCM lack objective measurement,and the complex mechanisms of action are difficult to comprehend,which hinders the application and internationalization of TCM.Recently,while breakthroughs have been made in utilizing methods such as network pharmacology and virtual screening for TCM research,the rise of machine learning(ML)has significantly enhanced their integration with TCM.This article introduces representative methodological cases in quality control,mechanism research,diagnosis,and treatment processes of TCM,revealing the potential applications of ML technology in TCM.Furthermore,the challenges faced by ML in TCM applications are summarized,and future directions are discussed.展开更多
Artificial intelligence(AI)serves as a key technology in global industrial transformation and technological restructuring and as the core driver of the fourth industrial revolution.Currently,deep learning techniques,s...Artificial intelligence(AI)serves as a key technology in global industrial transformation and technological restructuring and as the core driver of the fourth industrial revolution.Currently,deep learning techniques,such as convolutional neural networks,enable intelligent information collection in fields such as tongue and pulse diagnosis owing to their robust feature-processing capabilities.Natural language processing models,including long short-term memory and transformers,have been applied to traditional Chinese medicine(TCM)for diagnosis,syndrome differentiation,and prescription generation.Traditional machine learning algorithms,such as neural networks,support vector machines,and random forests,are also widely used in TCM diagnosis and treatment because of their strong regression and classification performance on small structured datasets.Future research on AI in TCM diagnosis and treatment may emphasize building large-scale,high-quality TCM datasets with unified criteria based on syndrome elements;identifying algorithms suited to TCM theoretical data distributions;and leveraging AI multimodal fusion and ensemble learning techniques for diverse raw features,such as images,text,and manually processed structured data,to increase the clinical efficacy of TCM diagnosis and treatment.展开更多
OBJECTIVE:To research the subtyping and treatment of depression by leveraging studying on extensive Traditional Chinese Medicine(TCM)experiences through artificial intelligence(AI).METHODS:We retrieved depression-rela...OBJECTIVE:To research the subtyping and treatment of depression by leveraging studying on extensive Traditional Chinese Medicine(TCM)experiences through artificial intelligence(AI).METHODS:We retrieved depression-related literature published from inception to April 2023 from databases.From these sources,we extracted symptoms,signs,and prescriptions associated with depression.By utilizing the tree number system in the medical subject headings(MeSH),we established a hierarchical relationship matrix for symptoms/signs,as well as depression sample fingerprints.Using an unsupervised clustering algorithm,we constructed a machine learning model for classifying depression patients.Furthermore,we conducted an analysis of medication rules for each depression cluster.RESULTS:We created a My Structured Query Language(MySQL)database containing datasets of depression-symptoms/signs and depression-herbs,through mining 3522 published clinical literatures on TCM diagnosis and treatment for depression.We established hierarchical relationships among symptoms/signs of depression patients.Our unsupervised clustering analysis revealed that depression patients could be classified into 9 subtypes,with each subtype corresponding to a specific treatment prescription.Notably,one of the depression subtypes was consistently treated by Qi-tonifying formulas and herbs.This finding was further supported by data from Qi-deficiency patients,as there was a high similarity in the top symptoms/signs shared between this subtype and Qi-deficiency diagnosed by TCM.CONCLUSIONS:This study identified the subtypes and TCM treatment of depression by using machine learning and text mining.展开更多
Tian et al present a timely machine learning(ML)model integrating biochemical and novel traditional Chinese medicine(TCM)indicators(tongue edge redness,greasy coating)to predict hepatic steatosis in high metabolic ris...Tian et al present a timely machine learning(ML)model integrating biochemical and novel traditional Chinese medicine(TCM)indicators(tongue edge redness,greasy coating)to predict hepatic steatosis in high metabolic risk patients.Their prospective cohort design and dual-feature selection(LASSO+RFE)culminating in an interpretable XGBoost model(area under the curve:0.82)represent a significant methodological advance.The inclusion of TCM diagnostics addresses metabolic dysfunction-associated fatty liver disease(MAFLD’s)multisystem heterogeneity-a key strength that bridges holistic medicine with precision analytics and underscores potential cost savings over imaging-dependent screening.However,critical limitations impede clinical translation.First,the model’s singlecenter validation(n=711)lacks external/generalizability testing across diverse populations,risking bias from local demographics.Second,MAFLD subtyping(e.g.,lean MAFLD,diabetic MAFLD)was omitted despite acknowledged disease heterogeneity;this overlooks distinct pathophysiologies and may limit utility in stratified care.Third,while TCM features ranked among the top predictors in SHAP analysis,their clinical interpretability remains nebulous without mechanistic links to metabolic dysregulation.To resolve these gaps,we propose external validation in multiethnic cohorts using the published feature set(e.g.,aspartate aminotransferase/alanine aminotransferase,low-density lipoprotein cholesterol,TCM tongue markers)to assess robustness.Subtype-specific modeling to capture MAFLD heterogeneity,potentially enhancing accuracy in highrisk subgroups.Probing TCM microbiome/metabolomic correlations to ground tongue phenotypes in biological pathways,elevating model credibility.Despite shortcomings,this work pioneers a low-cost screening paradigm.Future iterations addressing these issues could revolutionize early MAFLD detection in resource-limited settings.展开更多
In recent years, a large number of college students are using educational APPs to learn English. The author has deeply analyzed and explored the difference of learning by APPs and traditional classroom learning with t...In recent years, a large number of college students are using educational APPs to learn English. The author has deeply analyzed and explored the difference of learning by APPs and traditional classroom learning with the background of Constructivism. Learning by APPs and traditional classroom learning have their prospective advantages and disadvantages on learning time,space, contents, methods, efficiency and supervision. Learners can make full use of educational APPs, combining APPs with traditional classroom learning to realize blending learning and achieve high-efficiency.展开更多
OBJECTIVE:To assess the effect of case-based learning(CBL)in the education of Traditional Chinese Medicine(TCM).METHODS:The studies concerning TCM courses designed with CBL were included by searching the databases of ...OBJECTIVE:To assess the effect of case-based learning(CBL)in the education of Traditional Chinese Medicine(TCM).METHODS:The studies concerning TCM courses designed with CBL were included by searching the databases of EBSCO,Pubmed,Science Citation Index,China National Knowledge Infrastructure,Chongqing VIP database.The valid data was extracted in accordance with the included criteria.The quality of the studies was assessed with Gemma Flores-Masteo.RESULTS:A total of 22 articles were retrieved that met the selection criteria:one was of high quality;two were of low quality;the rest were categorized as moderate quality.The majority of the studiesdemonstrated the better effect produced by CBL,while a few studies showed no difference,compared with the didactic format.All included studies confirmed the favorable effect on learners'attitude,skills and ability.CONCLUSION:CBL showed the desirable results in achieving the goal of learning.Compared to didactic approach,it played a more active role in promoting students'competency.Since the quality of the articles on which the study was based was not so high,the findings still need further research to become substantiated.展开更多
Objective: To explore the application effect of flipped classroom combined with problem-based learning teaching method in clinical skills teaching of standardized training for resident doctors of traditional Chinese M...Objective: To explore the application effect of flipped classroom combined with problem-based learning teaching method in clinical skills teaching of standardized training for resident doctors of traditional Chinese Medicine. Methods: The study used the experimental control method. The study lasted from September to November 2022. The subjects of this study were 49 students of standardized training for resident doctors of traditional Chinese Medicine from grades 2020, 2021 and 2022 of Dazhou integrated TCM & Western Medicine Hospital. They were randomly divided into experiment group (25) and control group (24). The experiment group adopted flipped classroom combined with problem-based learning teaching method, and the control group adopted traditional teaching method. The teaching content was 4 basic clinical skill projects, including four diagnoses of traditional Chinese Medicine, cardiopulmonary resuscitation, dressing change procedure, acupuncture and massage. The evaluation method was carried out by comparing the students’ performance and a self-designed questionnaire was used to investigate the students’ evaluation of the teaching method. Results: The test scores of total scores in the experimental group (90.12 ± 5.89) were all higher than those in the control group (81.47 ± 7.96) (t = 4.53, P P Conclusions: The teaching process of the flipped classroom combined with problem-based learning teaching method is conducive to improving the efficiency of classroom teaching, cultivating students’ self-learning ability, and enhancing students’ willingness to learn.展开更多
As an information-rich collective, there are always some people who choose to take risks for some ulterior purpose and others are committed to finding ways to deal with database security threats. The purpose of databa...As an information-rich collective, there are always some people who choose to take risks for some ulterior purpose and others are committed to finding ways to deal with database security threats. The purpose of database security research is to prevent the database from being illegally used or destroyed. This paper introduces the main literature in the field of database security research in recent years. First of all, we classify these papers, the classification criteria </span><span style="font-size:12px;font-family:Verdana;">are</span><span style="font-size:12px;font-family:Verdana;"> the influencing factors of database security. Compared with the traditional and machine learning (ML) methods, some explanations of concepts are interspersed to make these methods easier to understand. Secondly, we find that the related research has achieved some gratifying results, but there are also some shortcomings, such as weak generalization, deviation from reality. Then, possible future work in this research is proposed. Finally, we summarize the main contribution.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
General Secretary Xi Jinping has proposed the new civilization concept of civilization exchange and mutual learning,and the high cultural self-confidence lies in deep civilization exchange and mutual learning.Chinese ...General Secretary Xi Jinping has proposed the new civilization concept of civilization exchange and mutual learning,and the high cultural self-confidence lies in deep civilization exchange and mutual learning.Chinese traditional culture is the concentrated expression of country and nation at the cultural and spiritual level.Under the background of civilization mutual learming,it should cultivate the ideological foundation of traditional culture,focus on diversified development of media,build a bridge of communication between countries,and finally realize the construc-tion of the human destiny community and cultural community of“beauty representing itself with diversity and integri-ty”between Chinese traditional culture and other cultures.展开更多
Background:With the rapid development of the world’s technology,the connection and integration between traditional medicine and modern machine learning technology are increasingly close.In this study,we aimed to anal...Background:With the rapid development of the world’s technology,the connection and integration between traditional medicine and modern machine learning technology are increasingly close.In this study,we aimed to analyze publications on machine learning in traditional medicine by using bibliometric methods and explore global trends in the field.Methods:Relevant research on machine learning in traditional medicine extracted from the Web of Science Core Collection database.Bibliometric analysis and visualization were performed using the Bibliometrix package in R software.Global trends,source journals,authorship,and thematic keywords analysis were performed in this study.Results:From 2012 to 2022,a total of 282 publications on machine learning in traditional medicine were identified and analyzed.The average annual growth rate of the publications was 13.35%.China had the largest contribution in this field(53.9%),followed by the United States(32.6%).IEEE Access had the largest number of published articles,with a total of 15 articles.Calvin Yu-Chian Chen,Xiao-juan Hu and Jue Wang were the main researchers in this field.Shanghai University of Traditional Chinese Medicine and University of California,San Francisco were the main research institutions.Conclusion:This study provides information on research trends in machine learning in traditional medicine to better understand research hotspots and future developments in this field.According to current global trends,the number of publications in this field will gradually increase.China currently dominated the field.Applied research of machine learning techniques may be the next hot topic in this field and deserves further attention.展开更多
The opinion research on traditional Chinese medicine during the coronavirus disease 2019(COVID-19)pandemic on microblog,a social network,took into account the national people’s fight against COVID-19—the research ba...The opinion research on traditional Chinese medicine during the coronavirus disease 2019(COVID-19)pandemic on microblog,a social network,took into account the national people’s fight against COVID-19—the research background—the strength of traditional Chinese medicine during the pandemic—the research topic—and the public opinion—the research object.The timeline was divided into three stages according to the overall heat change.In order to explore and compare people’s emotion and topics of concern on traditional Chinese medicine during the different stages of the pandemic,deep learning analysis methods such as emotional analysis and Latent Dirichlet Allocation analysis were used.This study found that the public’s positive“emotional composition”on traditional Chinese medicine significantly improved within the timeline,while the public’s autonomy was enhanced and the overall public opinion started to show an increased trend.展开更多
This paper combines the cultivation of innovation ability with the content of problem-based learning(PBL),analyzes the current situation of the traditional dress design course,discusses the problems existing in the cu...This paper combines the cultivation of innovation ability with the content of problem-based learning(PBL),analyzes the current situation of the traditional dress design course,discusses the problems existing in the cultivation of innovation ability of college and university traditional dress design,and searches for the strategies to improve students’innovation ability based on PBL.This paper argues that PBL can provide assistance to the teaching design of traditional dress design courses,which is conducive to improving students’innovation ability in traditional dress design and realizing the desired teaching effect.展开更多
Objective To develop a non-invasive predictive model for coronary artery stenosis severity based on adaptive multi-modal integration of traditional Chinese and western medicine data.Methods Clinical indicators,echocar...Objective To develop a non-invasive predictive model for coronary artery stenosis severity based on adaptive multi-modal integration of traditional Chinese and western medicine data.Methods Clinical indicators,echocardiographic data,traditional Chinese medicine(TCM)tongue manifestations,and facial features were collected from patients who underwent coro-nary computed tomography angiography(CTA)in the Cardiac Care Unit(CCU)of Shanghai Tenth People's Hospital between May 1,2023 and May 1,2024.An adaptive weighted multi-modal data fusion(AWMDF)model based on deep learning was constructed to predict the severity of coronary artery stenosis.The model was evaluated using metrics including accura-cy,precision,recall,F1 score,and the area under the receiver operating characteristic(ROC)curve(AUC).Further performance assessment was conducted through comparisons with six ensemble machine learning methods,data ablation,model component ablation,and various decision-level fusion strategies.Results A total of 158 patients were included in the study.The AWMDF model achieved ex-cellent predictive performance(AUC=0.973,accuracy=0.937,precision=0.937,recall=0.929,and F1 score=0.933).Compared with model ablation,data ablation experiments,and various traditional machine learning models,the AWMDF model demonstrated superior per-formance.Moreover,the adaptive weighting strategy outperformed alternative approaches,including simple weighting,averaging,voting,and fixed-weight schemes.Conclusion The AWMDF model demonstrates potential clinical value in the non-invasive prediction of coronary artery disease and could serve as a tool for clinical decision support.展开更多
Objective Rheumatoid arthritis(RA)is a systemic autoimmune disease that affects the small joints of the whole body and degrades the patients’quality of life.Zhengqing Fengtongning(ZF)is a traditional Chinese medicine...Objective Rheumatoid arthritis(RA)is a systemic autoimmune disease that affects the small joints of the whole body and degrades the patients’quality of life.Zhengqing Fengtongning(ZF)is a traditional Chinese medicine preparation used to treat RA.ZF may cause liver injury.In this study,we aimed to develop a prediction model for abnormal liver function caused by ZF.Methods This retrospective study collected data from multiple centers from January 2018 to April 2023.Abnormal liver function was set as the target variable according to the alanine transaminase(ALT)level.Features were screened through univariate analysis and sequential forward selection for modeling.Ten machine learning and deep learning models were compared to find the model that most effectively predicted liver function from the available data.Results This study included 1,913 eligible patients.The LightGBM model exhibited the best performance(accuracy=0.96)out of the 10 learning models.The predictive metrics of the LightGBM model were as follows:precision=0.99,recall rate=0.97,F1_score=0.98,area under the curve(AUC)=0.98,sensitivity=0.97 and specificity=0.85 for predicting ALT<40 U/L;precision=0.60,recall rate=0.83,F1_score=0.70,AUC=0.98,sensitivity=0.83 and specificity=0.97 for predicting 40≤ALT<80 U/L;and precision=0.83,recall rate=0.63,F1_score=0.71,AUC=0.97,sensitivity=0.63 and specificity=1.00 for predicting ALT≥80 U/L.ZF-induced abnormal liver function was found to be associated with high total cholesterol and triglyceride levels,the combination of TNF-αinhibitors,JAK inhibitors,methotrexate+nonsteroidal anti-inflammatory drugs,leflunomide,smoking,older age,and females in middle-age(45-65 years old).Conclusion This study developed a model for predicting ZF-induced abnormal liver function,which may help improve the safety of integrated administration of ZF and Western medicine.展开更多
Traditional Chinese medicine formula(TCMF)represents a fundamental component of Chinese medical practice,incorporating medical knowledge and practices from both Han Chinese and various ethnic minorities,while providin...Traditional Chinese medicine formula(TCMF)represents a fundamental component of Chinese medical practice,incorporating medical knowledge and practices from both Han Chinese and various ethnic minorities,while providing comprehensive insights into health and disease.The foundation of TCMF lies in its holistic approach,manifested through herbal compatibility theory,which has emerged from extensive clinical experience and evolved into a highly refined knowledge system.Within this framework,Chinese herbal medicines exhibit intricated characteristics,including multi-component interactions,diverse target sites,and varied biological pathways.These complexities pose significant challenges for understanding their molecular mechanisms.Contemporary advances in artificial intelligence(AI)are reshaping research in traditional Chinese medicine(TCM),offering immense potential to transform our understanding of the molecular mechanisms underlying TCMFs.This review explores the application of AI in uncovering these mechanisms,highlighting its role in compound absorption,distribution,metabolism,and excretion(ADME)prediction,molecular target identification,compound and target synergy recognition,pharmacological mechanisms exploration,and herbal formula optimization.Furthermore,the review discusses the challenges and opportunities in AI-assisted research on TCMF molecular mechanisms,promoting the modernization and globalization of TCM.展开更多
Inspection is the first of the four diagnoses.Skin inspection is not only an important part of the four diagnoses of Chinese medicine,but also the characteristic and essence of Chinese dermatology.The development of a...Inspection is the first of the four diagnoses.Skin inspection is not only an important part of the four diagnoses of Chinese medicine,but also the characteristic and essence of Chinese dermatology.The development of artificial intelligence(AI)technology provides an opportunity for the intelligent research of skin inspection in traditional Chinese medicine(TCM).This study aims to review the application status of artificial intelligence in TCM skin inspection,and summarize the research progress of various artificial intelligence technologies in skin lesion differentiation,syndrome differentiation and location differentiation,and propose future directions for AI empowerment in TCM dermatology.Based on the basic theory of Chinese medicine and skin clinical thinking of Chinese medicine,this article puts forward the prospect of AI empowerment from the aspects of macro and micro combination,point and surface combination,mind-body combination and time-space combination.This study discusses the existing problems and challenges in the intersection of AI and TCM dermatology.The intersection of AI and TCM dermatological inspection is at the developmental stage.AI has broad application prospects within TCM dermatology,but it also faces numerous challenges.While AI offers opportunities to modernize TCM dermatology,challenges such as aligning AI with TCM’s holistic principles and ensuring clinical relevance remain.Further research integrates AI with TCM skin inspection methods,the intricate connotations and applications of TCM dermatological Inspection will be fully realized,thereby providing reference for the development of artificial intelligence to help TCM dermatology diagnosis and treatment technology.展开更多
Objective To investigate morphological differences between obstructive and non-obstructive coronary artery disease(CAD)patients using computer-aided image analysis,and identify color and texture features for tradition...Objective To investigate morphological differences between obstructive and non-obstructive coronary artery disease(CAD)patients using computer-aided image analysis,and identify color and texture features for traditional Chinese medicine(TCM)syndrome differentiation.Methods This prospective study enrolled patients undergoing coronary computed tomography angiography(CTA)at the Affiliated Hospital of Liaoning University of Traditional Chinese Medicine between May 1,2024 and August 7,2025.Based on CTA results,patients were categorized into obstructive CAD and non-obstructive CAD groups.Standardized tongue images were acquired using a dedicated mobile application(Traditional Chinese Medicine Tongue Image-Assisted Diagnosis System)and analyzed for the overall tongue surface and three macroscopic features(tooth marks,fissures,and red dots)from which high-dimensional color and texture parameters were extracted.Multi-scale texture features were derived using spatial-domain Laplacian pyramid and frequency-domain wavelet transform methods.Dimensionality reduction and feature selection were performed using principal component analysis(PCA)and random forest with 5-fold cross-validation.Feature stability was assessed using Hodges-Lehmann estimator and Cliff’s δ.A multi-view XGBoost model was developed to differentiate the two groups and evaluated on a temporally independent validation set using accuracy and the area under the receiver operating characteristic curve(AUC).SHapley Additive exPlanations(SHAP)analysis was applied to interpret model decisions.Results This study analyzed 373 CAD patients,including 167 with obstructive CAD and 206 with non-obstructive CAD according to CTA results.The whole cohort was divided into training set(n=316,obstructive:non-obstructive=142:174)and validation set(n=57,obstructive:non-obstructive=25:32),with balanced baseline characteristics(P>0.05).Macroscopic tongue analysis revealed that patients with obstructive CAD had fewer tooth marks[odds ratio(OR)=0.43,P<0.05]and red dots(OR=0.46,P<0.05).High-dimensional color analysis identified pronounced intergroup differences,most notably a reduction in hue values in the hue-saturation-intensity(HSI)color space among obstructive CAD patients(Cliff’s δ=-0.31,P=2.72×10^(-6);Hodges-Lehmann estimator:-0.31).PCA results suggested that tongue surface features explained the highest proportion of variance(48.2%).Random forest screening identified 77 stable features across all tongue regions,with wavelet-transformed texture features demonstrating the highest importance.The multi-view XGBoost fusion model achieved an accuracy of 75%and an AUC of 0.779 in the independent validation set.SHAP analysis identified the wavelet-based feature-left-handed lower-level gray-level size zone matrix zone variance(LHL_glszm_ZoneVariance)as the top predictor,accounting for 40.6%of the model's decision variance,and indicated that 85.3%of the predictive power came from wavelet-based texture features.Conclusion This study has provided objective evidence for the TCM concept that“the tongue reflects the heart”by identifying distinct morphological and colorimetric tongue patterns in patients with obstructive CAD through artificial intelligence(AI)-driven image analysis,and the promising performance of the computational model suggests its potential as a non-invasive adjunctive tool for CAD assessment.展开更多
Objective:As an age-related neurodegenerative disease,the prevalence of mild cognitive impairment(MCI)increases with age.Within the framework of traditional Chinese medicine,spleen-kidney deficiency syndrome(SKDS)is r...Objective:As an age-related neurodegenerative disease,the prevalence of mild cognitive impairment(MCI)increases with age.Within the framework of traditional Chinese medicine,spleen-kidney deficiency syndrome(SKDS)is recognized as the most frequent MCI subtype.Due to the covert and gradual onset of MCI,in community settings it poses a significant challenge for patients and their families to discern between typical aging and pathological changes.There exists an urgent need to devise a preliminary diagnostic tool designed for community-residing older adults with MCI attributed to SKDS(MCI-SKDS).Methods:This investigation enrolled 312 elderly individuals diagnosed with MCI,who were randomly distributed into training and test datasets at a 3:1 ratio.Five machine learning methods,including logistic regression(LR),decision tree(DT),naive Bayes(NB),support vector machine(SVM),and gradient boosting(GB),were used to build a diagnostic prediction model for MCI-SKDS.Accuracy,sensitivity,specificity,precision,F1 score,and area under the curve were used to evaluate model performance.Furthermore,the clinical applicability of the model was evaluated through decision curve analysis(DCA).Results:The accuracy,precision,specificity and F1 score of the DT model performed best in the training set(test set),with scores of 0.904(0.845),0.875(0.795),0.973(0.875)and 0.973(0.875).The sensitivity of the training set(test set)of the SVM model performed best among the five models with a score of 0.865(0.821).The area under the curve of all five models was greater than 0.9 for the training dataset and greater than 0.8 for the test dataset.The DCA of all models showed good clinical application value.The study identified ten indicators that were significant predictors of MCI-SKDS.Conclusion:The risk prediction index derived from machine learning for the MCI-SKDS prediction model is simple and practical;the model demonstrates good predictive value and clinical applicability,and the DT model had the best performance.展开更多
As a new data management paradigm,knowledge graphs can integrate multiple data sources and achieve quick responses,reasoning and better predictions in drug discovery.Characterized by powerful contagion and a high rate...As a new data management paradigm,knowledge graphs can integrate multiple data sources and achieve quick responses,reasoning and better predictions in drug discovery.Characterized by powerful contagion and a high rate of morbidity and mortality,porcine reproductive and respiratory syndrome(PRRS)is a common infectious disease in the global swine industry that causes economically great losses.Traditional Chinese medicine(TCM)has advantages in low adverse effects and a relatively affordable cost of application,and TCM is therefore conceived as a possibility to treat PRRS under the current circumstance that there is a lack of safe and effective approaches.Here,we constructed a knowledge graph containing common biomedical data from humans and Sus Scrofa as well as information from thousands of TCMs.Subsequently,we validated the effectiveness of the Sus Scrofa knowledge graph by the t-SNE algorithm and selected the optimal model(i.e.,transR)from six typical models,namely,transE,transR,DistMult,ComplEx,RESCAL and RotatE,according to five indicators,namely,MRR,MR,HITS@1,HITS@3 and HITS@10.Based on embedding vectors trained by the optimal model,anti-PRRSV TCMs were predicted by two paths,namely,VHC-Herb and VHPC-Herb,and potential anti-PRRSVTCMs were identified by retrieving the HERB database according to the phar-macological properties corresponding to symptoms of PRRS.Ultimately,Dan Shen's(Salvia miltiorrhiza Bunge)capacity to resist PRRSV infection was validated by a cell experiment in which the inhibition rate of PRRSV exceeded90%when the concentrations of Dan Shen extract were 0.004,0.008,0.016 and 0.032 mg/mL.In summary,this is the first report on the Sus Scrofa knowledge graph including TCM information,and our study reflects the important application values of deep learning on graphs in the swine industry as well as providing accessible TCM resources for PRRS.展开更多
文摘Traditional Chinese medicine(TCM)is an ancient medical system distinctive and effective in treating cancer,depression,coronavirus disease 2019(COVID-19),and other diseases.However,the relatively abstract diagnostic methods of TCM lack objective measurement,and the complex mechanisms of action are difficult to comprehend,which hinders the application and internationalization of TCM.Recently,while breakthroughs have been made in utilizing methods such as network pharmacology and virtual screening for TCM research,the rise of machine learning(ML)has significantly enhanced their integration with TCM.This article introduces representative methodological cases in quality control,mechanism research,diagnosis,and treatment processes of TCM,revealing the potential applications of ML technology in TCM.Furthermore,the challenges faced by ML in TCM applications are summarized,and future directions are discussed.
基金supported by grants from the National Natural Science Foundation of China(Key Program)(No.82230124)Traditional Chinese Medicine Inheritance and Innovation“Ten million”talent project-Qihuang Project Chief Scientist Project(No.0201000401)+1 种基金State Administration of Traditional Chinese Medicine 2nd National Traditional Chinese Medicine Inheritance Studio Construction Project(Official Letter of the State Office of Traditional Chinese Medicine[2022]No.245)National Natural Science Foundation of China(General Program)(No.81974556).
文摘Artificial intelligence(AI)serves as a key technology in global industrial transformation and technological restructuring and as the core driver of the fourth industrial revolution.Currently,deep learning techniques,such as convolutional neural networks,enable intelligent information collection in fields such as tongue and pulse diagnosis owing to their robust feature-processing capabilities.Natural language processing models,including long short-term memory and transformers,have been applied to traditional Chinese medicine(TCM)for diagnosis,syndrome differentiation,and prescription generation.Traditional machine learning algorithms,such as neural networks,support vector machines,and random forests,are also widely used in TCM diagnosis and treatment because of their strong regression and classification performance on small structured datasets.Future research on AI in TCM diagnosis and treatment may emphasize building large-scale,high-quality TCM datasets with unified criteria based on syndrome elements;identifying algorithms suited to TCM theoretical data distributions;and leveraging AI multimodal fusion and ensemble learning techniques for diverse raw features,such as images,text,and manually processed structured data,to increase the clinical efficacy of TCM diagnosis and treatment.
基金Supported by the National Key Research and Development Plan:Regulatory Pathways and Mechanisms of Conception and Governor Vessels Surface Stimulation in the Treatment of “Uterus and Brain” Disorders (No. 2022YFC3500405)Taishan Scholar Youth Project of Shandong Province (No. tsqn202306188)+3 种基金the National Natural Science Foundation of China:Epigenetic Regulation of Vascular Neural Unit Function by Vascular Endothelial Histone Deacetylase:a New Antidepressant Application and Mechanism of Huangqi Guizhi Wuwu Decoction (No. 82274128)the National Natural Science Foundation of China:Study on the Anti-depression Mechanism of Electroacupuncture based on the Regulation of Biological Clock Gene in Prefrontal Cortex (No. 81973948)Joint Fund of Shandong Provincial Natural Science Foundation:High-Throughput Screening and Key Target Validation of Traditional Chinese Medicine Blood-Activating and Stasis-Resolving Components using a Vascular Microenvironment Simulation Chip (No. ZR2021LZY020)Student Research Training Program of Shandong University of Traditional Chinese Medicine:the Therapeutic Mechanism of Huangqi Guizhi Wuwu Decoction on Chronic Unpredictable Mild Stress Model Mice Based on the Endothelial Nitric Oxide Synthase-Nitric oxide Pathway Research of Vascular Endothelial Eells (No. 202210441008)
文摘OBJECTIVE:To research the subtyping and treatment of depression by leveraging studying on extensive Traditional Chinese Medicine(TCM)experiences through artificial intelligence(AI).METHODS:We retrieved depression-related literature published from inception to April 2023 from databases.From these sources,we extracted symptoms,signs,and prescriptions associated with depression.By utilizing the tree number system in the medical subject headings(MeSH),we established a hierarchical relationship matrix for symptoms/signs,as well as depression sample fingerprints.Using an unsupervised clustering algorithm,we constructed a machine learning model for classifying depression patients.Furthermore,we conducted an analysis of medication rules for each depression cluster.RESULTS:We created a My Structured Query Language(MySQL)database containing datasets of depression-symptoms/signs and depression-herbs,through mining 3522 published clinical literatures on TCM diagnosis and treatment for depression.We established hierarchical relationships among symptoms/signs of depression patients.Our unsupervised clustering analysis revealed that depression patients could be classified into 9 subtypes,with each subtype corresponding to a specific treatment prescription.Notably,one of the depression subtypes was consistently treated by Qi-tonifying formulas and herbs.This finding was further supported by data from Qi-deficiency patients,as there was a high similarity in the top symptoms/signs shared between this subtype and Qi-deficiency diagnosed by TCM.CONCLUSIONS:This study identified the subtypes and TCM treatment of depression by using machine learning and text mining.
文摘Tian et al present a timely machine learning(ML)model integrating biochemical and novel traditional Chinese medicine(TCM)indicators(tongue edge redness,greasy coating)to predict hepatic steatosis in high metabolic risk patients.Their prospective cohort design and dual-feature selection(LASSO+RFE)culminating in an interpretable XGBoost model(area under the curve:0.82)represent a significant methodological advance.The inclusion of TCM diagnostics addresses metabolic dysfunction-associated fatty liver disease(MAFLD’s)multisystem heterogeneity-a key strength that bridges holistic medicine with precision analytics and underscores potential cost savings over imaging-dependent screening.However,critical limitations impede clinical translation.First,the model’s singlecenter validation(n=711)lacks external/generalizability testing across diverse populations,risking bias from local demographics.Second,MAFLD subtyping(e.g.,lean MAFLD,diabetic MAFLD)was omitted despite acknowledged disease heterogeneity;this overlooks distinct pathophysiologies and may limit utility in stratified care.Third,while TCM features ranked among the top predictors in SHAP analysis,their clinical interpretability remains nebulous without mechanistic links to metabolic dysregulation.To resolve these gaps,we propose external validation in multiethnic cohorts using the published feature set(e.g.,aspartate aminotransferase/alanine aminotransferase,low-density lipoprotein cholesterol,TCM tongue markers)to assess robustness.Subtype-specific modeling to capture MAFLD heterogeneity,potentially enhancing accuracy in highrisk subgroups.Probing TCM microbiome/metabolomic correlations to ground tongue phenotypes in biological pathways,elevating model credibility.Despite shortcomings,this work pioneers a low-cost screening paradigm.Future iterations addressing these issues could revolutionize early MAFLD detection in resource-limited settings.
文摘In recent years, a large number of college students are using educational APPs to learn English. The author has deeply analyzed and explored the difference of learning by APPs and traditional classroom learning with the background of Constructivism. Learning by APPs and traditional classroom learning have their prospective advantages and disadvantages on learning time,space, contents, methods, efficiency and supervision. Learners can make full use of educational APPs, combining APPs with traditional classroom learning to realize blending learning and achieve high-efficiency.
基金Supported by "Twelve-five" Scientific Research Study on Education from Chinese Academy of Higher Education(No.11YB032)by Scientific Research Study on Education from Sichuan Academy of Higher Education(No.11SC-007)by Key research project on teaching reform from Chengdu University of Traditional Chinese Medicine(No.JGZD201001)
文摘OBJECTIVE:To assess the effect of case-based learning(CBL)in the education of Traditional Chinese Medicine(TCM).METHODS:The studies concerning TCM courses designed with CBL were included by searching the databases of EBSCO,Pubmed,Science Citation Index,China National Knowledge Infrastructure,Chongqing VIP database.The valid data was extracted in accordance with the included criteria.The quality of the studies was assessed with Gemma Flores-Masteo.RESULTS:A total of 22 articles were retrieved that met the selection criteria:one was of high quality;two were of low quality;the rest were categorized as moderate quality.The majority of the studiesdemonstrated the better effect produced by CBL,while a few studies showed no difference,compared with the didactic format.All included studies confirmed the favorable effect on learners'attitude,skills and ability.CONCLUSION:CBL showed the desirable results in achieving the goal of learning.Compared to didactic approach,it played a more active role in promoting students'competency.Since the quality of the articles on which the study was based was not so high,the findings still need further research to become substantiated.
文摘Objective: To explore the application effect of flipped classroom combined with problem-based learning teaching method in clinical skills teaching of standardized training for resident doctors of traditional Chinese Medicine. Methods: The study used the experimental control method. The study lasted from September to November 2022. The subjects of this study were 49 students of standardized training for resident doctors of traditional Chinese Medicine from grades 2020, 2021 and 2022 of Dazhou integrated TCM & Western Medicine Hospital. They were randomly divided into experiment group (25) and control group (24). The experiment group adopted flipped classroom combined with problem-based learning teaching method, and the control group adopted traditional teaching method. The teaching content was 4 basic clinical skill projects, including four diagnoses of traditional Chinese Medicine, cardiopulmonary resuscitation, dressing change procedure, acupuncture and massage. The evaluation method was carried out by comparing the students’ performance and a self-designed questionnaire was used to investigate the students’ evaluation of the teaching method. Results: The test scores of total scores in the experimental group (90.12 ± 5.89) were all higher than those in the control group (81.47 ± 7.96) (t = 4.53, P P Conclusions: The teaching process of the flipped classroom combined with problem-based learning teaching method is conducive to improving the efficiency of classroom teaching, cultivating students’ self-learning ability, and enhancing students’ willingness to learn.
文摘As an information-rich collective, there are always some people who choose to take risks for some ulterior purpose and others are committed to finding ways to deal with database security threats. The purpose of database security research is to prevent the database from being illegally used or destroyed. This paper introduces the main literature in the field of database security research in recent years. First of all, we classify these papers, the classification criteria </span><span style="font-size:12px;font-family:Verdana;">are</span><span style="font-size:12px;font-family:Verdana;"> the influencing factors of database security. Compared with the traditional and machine learning (ML) methods, some explanations of concepts are interspersed to make these methods easier to understand. Secondly, we find that the related research has achieved some gratifying results, but there are also some shortcomings, such as weak generalization, deviation from reality. Then, possible future work in this research is proposed. Finally, we summarize the main contribution.
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.
基金Phased achievement of project under National Social Science Found-Research on Countermeasures of Multilevel Flow and Digital Communication of Targeted Poverty Alleviation Policy Information(16CXW027).
文摘General Secretary Xi Jinping has proposed the new civilization concept of civilization exchange and mutual learning,and the high cultural self-confidence lies in deep civilization exchange and mutual learning.Chinese traditional culture is the concentrated expression of country and nation at the cultural and spiritual level.Under the background of civilization mutual learming,it should cultivate the ideological foundation of traditional culture,focus on diversified development of media,build a bridge of communication between countries,and finally realize the construc-tion of the human destiny community and cultural community of“beauty representing itself with diversity and integri-ty”between Chinese traditional culture and other cultures.
文摘Background:With the rapid development of the world’s technology,the connection and integration between traditional medicine and modern machine learning technology are increasingly close.In this study,we aimed to analyze publications on machine learning in traditional medicine by using bibliometric methods and explore global trends in the field.Methods:Relevant research on machine learning in traditional medicine extracted from the Web of Science Core Collection database.Bibliometric analysis and visualization were performed using the Bibliometrix package in R software.Global trends,source journals,authorship,and thematic keywords analysis were performed in this study.Results:From 2012 to 2022,a total of 282 publications on machine learning in traditional medicine were identified and analyzed.The average annual growth rate of the publications was 13.35%.China had the largest contribution in this field(53.9%),followed by the United States(32.6%).IEEE Access had the largest number of published articles,with a total of 15 articles.Calvin Yu-Chian Chen,Xiao-juan Hu and Jue Wang were the main researchers in this field.Shanghai University of Traditional Chinese Medicine and University of California,San Francisco were the main research institutions.Conclusion:This study provides information on research trends in machine learning in traditional medicine to better understand research hotspots and future developments in this field.According to current global trends,the number of publications in this field will gradually increase.China currently dominated the field.Applied research of machine learning techniques may be the next hot topic in this field and deserves further attention.
文摘The opinion research on traditional Chinese medicine during the coronavirus disease 2019(COVID-19)pandemic on microblog,a social network,took into account the national people’s fight against COVID-19—the research background—the strength of traditional Chinese medicine during the pandemic—the research topic—and the public opinion—the research object.The timeline was divided into three stages according to the overall heat change.In order to explore and compare people’s emotion and topics of concern on traditional Chinese medicine during the different stages of the pandemic,deep learning analysis methods such as emotional analysis and Latent Dirichlet Allocation analysis were used.This study found that the public’s positive“emotional composition”on traditional Chinese medicine significantly improved within the timeline,while the public’s autonomy was enhanced and the overall public opinion started to show an increased trend.
文摘This paper combines the cultivation of innovation ability with the content of problem-based learning(PBL),analyzes the current situation of the traditional dress design course,discusses the problems existing in the cultivation of innovation ability of college and university traditional dress design,and searches for the strategies to improve students’innovation ability based on PBL.This paper argues that PBL can provide assistance to the teaching design of traditional dress design courses,which is conducive to improving students’innovation ability in traditional dress design and realizing the desired teaching effect.
基金Construction Program of the Key Discipline of State Administration of Traditional Chinese Medicine of China(ZYYZDXK-2023069)Research Project of Shanghai Municipal Health Commission (2024QN018)Shanghai University of Traditional Chinese Medicine Science and Technology Development Program (23KFL005)。
文摘Objective To develop a non-invasive predictive model for coronary artery stenosis severity based on adaptive multi-modal integration of traditional Chinese and western medicine data.Methods Clinical indicators,echocardiographic data,traditional Chinese medicine(TCM)tongue manifestations,and facial features were collected from patients who underwent coro-nary computed tomography angiography(CTA)in the Cardiac Care Unit(CCU)of Shanghai Tenth People's Hospital between May 1,2023 and May 1,2024.An adaptive weighted multi-modal data fusion(AWMDF)model based on deep learning was constructed to predict the severity of coronary artery stenosis.The model was evaluated using metrics including accura-cy,precision,recall,F1 score,and the area under the receiver operating characteristic(ROC)curve(AUC).Further performance assessment was conducted through comparisons with six ensemble machine learning methods,data ablation,model component ablation,and various decision-level fusion strategies.Results A total of 158 patients were included in the study.The AWMDF model achieved ex-cellent predictive performance(AUC=0.973,accuracy=0.937,precision=0.937,recall=0.929,and F1 score=0.933).Compared with model ablation,data ablation experiments,and various traditional machine learning models,the AWMDF model demonstrated superior per-formance.Moreover,the adaptive weighting strategy outperformed alternative approaches,including simple weighting,averaging,voting,and fixed-weight schemes.Conclusion The AWMDF model demonstrates potential clinical value in the non-invasive prediction of coronary artery disease and could serve as a tool for clinical decision support.
基金supported by the Budgeted Fund of Shanghai University of Traditional Chinese Medicine(Natural Science)(No.2021LK037)the Open Project of Qinghai Province Key Laboratory of Tibetan Medicine Pharmacology and Safety Evaluation(No.2021-ZY-03).
文摘Objective Rheumatoid arthritis(RA)is a systemic autoimmune disease that affects the small joints of the whole body and degrades the patients’quality of life.Zhengqing Fengtongning(ZF)is a traditional Chinese medicine preparation used to treat RA.ZF may cause liver injury.In this study,we aimed to develop a prediction model for abnormal liver function caused by ZF.Methods This retrospective study collected data from multiple centers from January 2018 to April 2023.Abnormal liver function was set as the target variable according to the alanine transaminase(ALT)level.Features were screened through univariate analysis and sequential forward selection for modeling.Ten machine learning and deep learning models were compared to find the model that most effectively predicted liver function from the available data.Results This study included 1,913 eligible patients.The LightGBM model exhibited the best performance(accuracy=0.96)out of the 10 learning models.The predictive metrics of the LightGBM model were as follows:precision=0.99,recall rate=0.97,F1_score=0.98,area under the curve(AUC)=0.98,sensitivity=0.97 and specificity=0.85 for predicting ALT<40 U/L;precision=0.60,recall rate=0.83,F1_score=0.70,AUC=0.98,sensitivity=0.83 and specificity=0.97 for predicting 40≤ALT<80 U/L;and precision=0.83,recall rate=0.63,F1_score=0.71,AUC=0.97,sensitivity=0.63 and specificity=1.00 for predicting ALT≥80 U/L.ZF-induced abnormal liver function was found to be associated with high total cholesterol and triglyceride levels,the combination of TNF-αinhibitors,JAK inhibitors,methotrexate+nonsteroidal anti-inflammatory drugs,leflunomide,smoking,older age,and females in middle-age(45-65 years old).Conclusion This study developed a model for predicting ZF-induced abnormal liver function,which may help improve the safety of integrated administration of ZF and Western medicine.
基金supported by the National Key R&D Program of China(No.2022YFC3502005)the three-year Action Plan for Shanghai TCM Development and Inheritance Program[No.ZY(2021-2023)-0401]the National Natural Science Foundation of China(No.82104521)。
文摘Traditional Chinese medicine formula(TCMF)represents a fundamental component of Chinese medical practice,incorporating medical knowledge and practices from both Han Chinese and various ethnic minorities,while providing comprehensive insights into health and disease.The foundation of TCMF lies in its holistic approach,manifested through herbal compatibility theory,which has emerged from extensive clinical experience and evolved into a highly refined knowledge system.Within this framework,Chinese herbal medicines exhibit intricated characteristics,including multi-component interactions,diverse target sites,and varied biological pathways.These complexities pose significant challenges for understanding their molecular mechanisms.Contemporary advances in artificial intelligence(AI)are reshaping research in traditional Chinese medicine(TCM),offering immense potential to transform our understanding of the molecular mechanisms underlying TCMFs.This review explores the application of AI in uncovering these mechanisms,highlighting its role in compound absorption,distribution,metabolism,and excretion(ADME)prediction,molecular target identification,compound and target synergy recognition,pharmacological mechanisms exploration,and herbal formula optimization.Furthermore,the review discusses the challenges and opportunities in AI-assisted research on TCMF molecular mechanisms,promoting the modernization and globalization of TCM.
基金supported by the Capital’s Funds for Health Improvement and Research(No.2022-2-1161).
文摘Inspection is the first of the four diagnoses.Skin inspection is not only an important part of the four diagnoses of Chinese medicine,but also the characteristic and essence of Chinese dermatology.The development of artificial intelligence(AI)technology provides an opportunity for the intelligent research of skin inspection in traditional Chinese medicine(TCM).This study aims to review the application status of artificial intelligence in TCM skin inspection,and summarize the research progress of various artificial intelligence technologies in skin lesion differentiation,syndrome differentiation and location differentiation,and propose future directions for AI empowerment in TCM dermatology.Based on the basic theory of Chinese medicine and skin clinical thinking of Chinese medicine,this article puts forward the prospect of AI empowerment from the aspects of macro and micro combination,point and surface combination,mind-body combination and time-space combination.This study discusses the existing problems and challenges in the intersection of AI and TCM dermatology.The intersection of AI and TCM dermatological inspection is at the developmental stage.AI has broad application prospects within TCM dermatology,but it also faces numerous challenges.While AI offers opportunities to modernize TCM dermatology,challenges such as aligning AI with TCM’s holistic principles and ensuring clinical relevance remain.Further research integrates AI with TCM skin inspection methods,the intricate connotations and applications of TCM dermatological Inspection will be fully realized,thereby providing reference for the development of artificial intelligence to help TCM dermatology diagnosis and treatment technology.
基金Youth Qihuang Scholar Support Project(20201A2180)。
文摘Objective To investigate morphological differences between obstructive and non-obstructive coronary artery disease(CAD)patients using computer-aided image analysis,and identify color and texture features for traditional Chinese medicine(TCM)syndrome differentiation.Methods This prospective study enrolled patients undergoing coronary computed tomography angiography(CTA)at the Affiliated Hospital of Liaoning University of Traditional Chinese Medicine between May 1,2024 and August 7,2025.Based on CTA results,patients were categorized into obstructive CAD and non-obstructive CAD groups.Standardized tongue images were acquired using a dedicated mobile application(Traditional Chinese Medicine Tongue Image-Assisted Diagnosis System)and analyzed for the overall tongue surface and three macroscopic features(tooth marks,fissures,and red dots)from which high-dimensional color and texture parameters were extracted.Multi-scale texture features were derived using spatial-domain Laplacian pyramid and frequency-domain wavelet transform methods.Dimensionality reduction and feature selection were performed using principal component analysis(PCA)and random forest with 5-fold cross-validation.Feature stability was assessed using Hodges-Lehmann estimator and Cliff’s δ.A multi-view XGBoost model was developed to differentiate the two groups and evaluated on a temporally independent validation set using accuracy and the area under the receiver operating characteristic curve(AUC).SHapley Additive exPlanations(SHAP)analysis was applied to interpret model decisions.Results This study analyzed 373 CAD patients,including 167 with obstructive CAD and 206 with non-obstructive CAD according to CTA results.The whole cohort was divided into training set(n=316,obstructive:non-obstructive=142:174)and validation set(n=57,obstructive:non-obstructive=25:32),with balanced baseline characteristics(P>0.05).Macroscopic tongue analysis revealed that patients with obstructive CAD had fewer tooth marks[odds ratio(OR)=0.43,P<0.05]and red dots(OR=0.46,P<0.05).High-dimensional color analysis identified pronounced intergroup differences,most notably a reduction in hue values in the hue-saturation-intensity(HSI)color space among obstructive CAD patients(Cliff’s δ=-0.31,P=2.72×10^(-6);Hodges-Lehmann estimator:-0.31).PCA results suggested that tongue surface features explained the highest proportion of variance(48.2%).Random forest screening identified 77 stable features across all tongue regions,with wavelet-transformed texture features demonstrating the highest importance.The multi-view XGBoost fusion model achieved an accuracy of 75%and an AUC of 0.779 in the independent validation set.SHAP analysis identified the wavelet-based feature-left-handed lower-level gray-level size zone matrix zone variance(LHL_glszm_ZoneVariance)as the top predictor,accounting for 40.6%of the model's decision variance,and indicated that 85.3%of the predictive power came from wavelet-based texture features.Conclusion This study has provided objective evidence for the TCM concept that“the tongue reflects the heart”by identifying distinct morphological and colorimetric tongue patterns in patients with obstructive CAD through artificial intelligence(AI)-driven image analysis,and the promising performance of the computational model suggests its potential as a non-invasive adjunctive tool for CAD assessment.
基金funded by the National Natural Science Foundation of China(No.82405530,81973921 and 72374068)the Science and Technology Research Project of Hubei Provincial Department of Education(No.B2023098)。
文摘Objective:As an age-related neurodegenerative disease,the prevalence of mild cognitive impairment(MCI)increases with age.Within the framework of traditional Chinese medicine,spleen-kidney deficiency syndrome(SKDS)is recognized as the most frequent MCI subtype.Due to the covert and gradual onset of MCI,in community settings it poses a significant challenge for patients and their families to discern between typical aging and pathological changes.There exists an urgent need to devise a preliminary diagnostic tool designed for community-residing older adults with MCI attributed to SKDS(MCI-SKDS).Methods:This investigation enrolled 312 elderly individuals diagnosed with MCI,who were randomly distributed into training and test datasets at a 3:1 ratio.Five machine learning methods,including logistic regression(LR),decision tree(DT),naive Bayes(NB),support vector machine(SVM),and gradient boosting(GB),were used to build a diagnostic prediction model for MCI-SKDS.Accuracy,sensitivity,specificity,precision,F1 score,and area under the curve were used to evaluate model performance.Furthermore,the clinical applicability of the model was evaluated through decision curve analysis(DCA).Results:The accuracy,precision,specificity and F1 score of the DT model performed best in the training set(test set),with scores of 0.904(0.845),0.875(0.795),0.973(0.875)and 0.973(0.875).The sensitivity of the training set(test set)of the SVM model performed best among the five models with a score of 0.865(0.821).The area under the curve of all five models was greater than 0.9 for the training dataset and greater than 0.8 for the test dataset.The DCA of all models showed good clinical application value.The study identified ten indicators that were significant predictors of MCI-SKDS.Conclusion:The risk prediction index derived from machine learning for the MCI-SKDS prediction model is simple and practical;the model demonstrates good predictive value and clinical applicability,and the DT model had the best performance.
基金supported by the China Fundamental Research Funds for the Central Universities(No.2662022XXYJ001,2662022JC004,2662023XXPY005)。
文摘As a new data management paradigm,knowledge graphs can integrate multiple data sources and achieve quick responses,reasoning and better predictions in drug discovery.Characterized by powerful contagion and a high rate of morbidity and mortality,porcine reproductive and respiratory syndrome(PRRS)is a common infectious disease in the global swine industry that causes economically great losses.Traditional Chinese medicine(TCM)has advantages in low adverse effects and a relatively affordable cost of application,and TCM is therefore conceived as a possibility to treat PRRS under the current circumstance that there is a lack of safe and effective approaches.Here,we constructed a knowledge graph containing common biomedical data from humans and Sus Scrofa as well as information from thousands of TCMs.Subsequently,we validated the effectiveness of the Sus Scrofa knowledge graph by the t-SNE algorithm and selected the optimal model(i.e.,transR)from six typical models,namely,transE,transR,DistMult,ComplEx,RESCAL and RotatE,according to five indicators,namely,MRR,MR,HITS@1,HITS@3 and HITS@10.Based on embedding vectors trained by the optimal model,anti-PRRSV TCMs were predicted by two paths,namely,VHC-Herb and VHPC-Herb,and potential anti-PRRSVTCMs were identified by retrieving the HERB database according to the phar-macological properties corresponding to symptoms of PRRS.Ultimately,Dan Shen's(Salvia miltiorrhiza Bunge)capacity to resist PRRSV infection was validated by a cell experiment in which the inhibition rate of PRRSV exceeded90%when the concentrations of Dan Shen extract were 0.004,0.008,0.016 and 0.032 mg/mL.In summary,this is the first report on the Sus Scrofa knowledge graph including TCM information,and our study reflects the important application values of deep learning on graphs in the swine industry as well as providing accessible TCM resources for PRRS.
