Objective: To address the dual challenges of long-tail distribution and feature sparsity in traditional Chinese medicine (TCM) syndrome differentiation within real clinical settings, we propose a data-efficient learning framework enhanced by knowledge graphs. Methods: We developed Agent-GNN, a three-stage decoupled learning framework, and validated it on the Traditional Chinese Medicine Syndrome Diagnosis (TCM-SD) dataset containing 54 152 clinical records across 148 syndrome categories. First, we constructed a comprehensive medical knowledge graph encoding the complete TCM reasoning system. Second, we proposed a Functional Patient Profiling (FPP) method that utilizes large language models (LLMs) combined with Graph Retrieval-Augmented Generation (RAG) to extract structured symptom-etiology-pathogenesis subgraphs from medical records. Third, we employed heterogeneous graph neural networks to learn structured combination patterns explicitly. We compared our method against multiple baselines including BERT, ZY-BERT, ZY-BERT + Know, GAT, and GPT-4 Few-shot, using macro-F1 score as the primary evaluation metric. Additionally, ablation experiments were conducted to validate the contribution of each key component to model performance. Results: Agent-GNN achieved an overall macro-F1 score of 72.4%, representing an 8.7 percentage points improvement over ZY-BERT + Know (63.7%), the strongest baseline among traditional methods. For long-tail syndromes with fewer than 10 samples, Agent-GNN reached a macro-F1 score of 58.6%, compared with 39.3% for ZY-BERT + Know and 41.2% for GPT-4 Few-shot, representing relative improvements of 49.2% and 42.2%, respectively. Ablation experiments confirmed that the explicit modeling of etiology-pathogenesis nodes contributed 12.4 percentage points to this enhanced long-tail syndrome performance. Conclusion This study proposes Agent-GNN, a knowledge graph-enhanced framework that effectively addresses the long-tail distribution challenge in TCM syndrome differentiation. By explicitly modeling manifestation-mechanism-essence patterns through structured knowledge graphs, our approach achieves superior performance in data-scarce scenarios while providing interpretable reasoning paths for TCM intelligent diagnosis.

This paper summarizes the clinical experience of professor ZHANG Zhiwen in differentiating and treating chronic obstructive pulmonary disease （COPD） at stages， with the method of diffusing and clearing to unblock the channel pathways， while warming and subduing to restore the original qi. Professor ZHANG posits that the core pathogenesis of this disease lies in depletion of original qi， leading to impaired qi transformation of sanjiao （三焦）， and obstruction of channel pathways. Accordingly， a staged differentiation and treatment strategy based on the methods of diffusing and clearing to unblock the channel pathways， while warming and subduing to restore the original qi has been proposed. COPD was divided into the acute exacerbation stage and the stable stage. In acute exacerbation stage， priority is given to diffusing lung and clearing up turbidity， scatter and dredge the channel passages， using the self-formulated Xuandi Tongsui Formula （宣涤通隧方）. During the stable stage， the treatment should focus on simultaneously regulating sanjiao， warming and subduing yang to restore the original qi， using the self-formulated Wenqian Guiyuan Formula （温潜归元方）. In addition， the principles of carefully protecting mild yang， avoiding excessive astringent medicinals， and restoring respiration to its root are emphasized throughout the entire course of disease prevention， treatment， and rehabilitation.

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 introduced the attention mechanism (AM), grey wolf optimizer (GWO), and particle swarm optimization (PSO). The models’ performance was evaluated before and after optimization using the F1-score, accuracy, precision, and recall. Results: The research analyzed a total of 31 655 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 operating characteristic (ROC) curve (AUC) values exceeding 70% and 0.78, respectively. This indicates that while the model possesses a certain level of constitutional differentiation ability, 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 stagnation 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, demonstrated an improved prediction accuracy rate of 54%. Conclusion This study constructed MLP, LSTM, and DBN models for predicting TCM constitution 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 establishment and optimisation strategies of TCM constitution prediction models,and a novel idea for the treatment of non-disease.

Objective This study aimed to explore the influencing factors of dynamic changes in tradi-tional Chinese medicine(TCM)constitution based on general statistics,Apriori-DEMATEL algorithm,and DoWhy causal inference framework methods. Methods Dynamic collection of TCM constitution identification data was conducted from the population aged 18-60,containing collection time and constitution type,and 11 constitu-tion influencing factors including dietary habit,sleeping habit,sleeping duration,exercise habit,emotion state,stress level,living environment,work/life calamity,family atmosphere,business trip frequency,and overtime situation.General statistical analysis was used to ana-lyze the relative percentage of corresponding influencing factors of different types of constitu-tion changes,the Apriori-DEMATEL algorithm was used to analyze the correlation between 11 constitution influencing factors such as dietary habit and constitution changes,and the DoWhy causal inference framework was used to analyze the causality between dietary habit,sleeping habit,sleeping duration,exercise habit,emotion state,and stress level,explore the frequency of constitution type transformation-change factors,and determine the key influ-encing factors causing dynamic changes in constitution type. Results After preprocessing,13 536 valid data points were obtained.Based on the Apriori-DEMATEL algorithm,the factors were divided into six original factors including dietary habit,sleeping habit,sleeping duration,exercise habit,emotion state,and stress level,and five re-sult factors including living environment,work/life calamity,family atmosphere,business trip frequency,and overtime situation.Combining with general statistics,we found that among the original factors,changes in dietary habit,sleeping habit,sleeping duration,and stress lev-el had a greater impact on other factors.In the process of constitution conditioning,attention should be paid to these four factors to maintain constitution balance.Among the five result factors,the absolute values of work/life calamity and family atmosphere were relatively large,indicating that these two factors were easily influenced by other factors.The dietary habit,sleeping habit,sleeping duration,exercise habit,emotion state,and stress level have higher centrality in changes,indicating that these six factors had important in constitution changes.According to the statistical frequency of constitution changes corresponding to each factor,we found that the changes of these six factors accounted for a large proportion of the constitu-tion transformation frequency among Qi deficiency constitution,balanced constitution,and allergic constitution,indicating that the changes of these six factors played an important role in the changes of the three constitution types.Combined with the results of the Apriori-DEMATEL algorithm,and DoWhy causal inference framework analysis,it was inferred that dietary habit and sleeping duration indirectly lead to constitution changes by affecting the changes of other factors. Conclusion This study explored the influencing factors of dynamic changes in TCM constitu-tion from the perspective of dynamic data and multiple analysis methods,and the results showed that the changes of dietary habit,sleeping habit,sleeping duration,exercise habit,emotion state,and stress level had a great impact on the changes of Qi deficiency constitu-tion,balanced constitution and allergic constitution.Attention should be paid to the changes of these six factors in daily life,and corresponding improvement plans should be formulated to reduce the probability of transforming into biased constitution.Our study also provided data support and objective analysis reference for the analysis of influencing factors of dynam-ic changes in TCM constitution types.

By applying "homeostasis" to the study of the meridian and collateral system， the concept of meridian and collateral homeostasis has been proposed which refers to a balanced and stable state of meridian and collateral system， and plays an important role in maintaining body health and can provide a reference for the diagnosis and treatment of diseases. Phenomics realizes the cross-scale correlation from micro-phenotypic data， such as genome， proteome， and metabolome， to macro-phenotypic data， such as physiological state， behavioral activities， and external manifestations. From the perspective of phenomics， this paper proposes a meridian and collateral homeostasis dynamic mapping model of "macroscopic signs and microscopic expression". This model combines macro signs such as the four examinations of traditional Chinese medicine （TCM）， biophysical indicators of acupoints， and micro expression information such as genes， proteins， and metabolism， and systematically investigates the relationship between meridian and collateral homeostasis and health and disease， thereby providing ideas and references for the identification of pre-disease states as well as precise diagnosis and treatment in TCM.

Interpretation of syndrome differentiation is a necessary topic for the research on intelligent syndrome differentiation. The syndrome differentiation system of traditional Chinese medicine （TCM） is huge， with diverse and interrelated interpretations， and non-standardized linguistic expressions， while detailed interpretations involve complex basic theories and literature of TCM， resulting in a challenging organization of the system. From the perspective of text mining， this article analyzed the limitations of existing intelligent syndrome differentiation research， clarified the importance and necessity of constructing a syndrome differentiation interpretation system， and proposed the idea of constructing an interpretation system including conceptual system， logical system， evaluation system， and visualization presentation. Future research can continue to refine the structure， logical and semantic relationships， as well as evaluation methods of the interpretation system based on this ideas， present concrete examples， and build the interpretation corpus by utilizing various data sources such as textbooks， medical records， medical discourse， and papers， to provide technicians with standard reference and data support for TCM syndrome differentiation interpretation.

Objective To solve the problem of large data volume,multiple dimensions and low value for assisting traditional Chinese medicine clinical diagnosis in home health perception layer devices.Methods Based on the principles of traditional Chinese medicine diagnosis,this paper divides home health data into three types:complementary,redundant,and collaborative,and proposes a solution for data fusion at the levels of device data,home events,and traditional Chinese medicine symptoms.Results The proposed data fusion solution in this paper enables the data collected by various devices in the home environment to work together,extracts home data that is more valuable for traditional Chinese medicine diagnosis,and reduces the real-time pressure on the home network bandwidth caused by the sensors on the home side.Conclusion The construction of an open IoT ecosystem for home health based on multiple devices is a huge project,which includes the construction of perception layer hardware,data cleaning,fusion,normalization,labeling,modeling,and other aspects.This paper focuses on the idea of home health data fusion,which can provide directions for cleaning up heterogeneous data from multiple sources at home and also provide ideas for subsequent data labeling and modeling with traditional Chinese medicine characteristics,thus providing more valuable decision-making assistance for traditional Chinese medicine clinical practice.

Systematic research on the quality evaluation methods of Chinese medicinal materials is an intrinsic requirement,which is beneficial to the clinical application of traditional Chinese medicine and the sound development of traditional Chinese medicinal industry.The intrinsic quality evaluation methods of Chinese medicinal materials have developed from chemical fingerprint to quality marker of Chinese medicinal materials,and it represents that the evaluation mode has developed from chemical component as research index to the combination of chemical component and pharmacodynamic components,chemical component and biological activity.The extrinsic character evaluation method of Chinese medicinal materials from"Quality Evaluation Through Morphological Identification"to the application of intelligent sensory technology make up for the accumulation of objective data.The trend of quality evaluation of the intrinsic quality and extrinsic traits of Chinese herbal medicines conform to the innovation strategy called integrated quality control of TCMs.This paper reviews the development and evolution of the research on the intrinsic quality and extrinsic traits of Chinese herbal medicines,systematically expounds the research status and development trend of"intrinsic and extrinsic combination",and facilitate the establishment of quality evaluation system of Chinese herbal medicines with standardization and informatization characteristics in accordance with the theory of traditional Chinese medicine.

Traditional Chinese medicine(TCM)plays a unique role in preventive treatment of disease,treatment of chronic disease and recovery.Integrating modern science and technology and methods to carry out integrated and innovative research will help to bring the value of traditional Chinese medicine into full play.In recent years,with the rapid development of digital therapeutics such as artificial intelligence and wearable devices,digital therapeutics have emerged.Under the guidance of TCM theory,TCM integrates digital therapeutics technology methods,personalized treatment of patients through advanced program-driven software or combined with hardware devices,and improves patient compliance,gives full play to the advantages of TCM.This paper uses CiteSpace to visualize the research hotspots of digital therapeutics,combined with the literature and the website data of the Food and Drug Administration and the National Medical Products Administration,to sort out the application of digital therapeutics in the prevention,treatment and recovery of diseases,analyze the key technologies and core theories in the current application of digital therapeutics.And taking TCM pentatotherapy,kinesiatrics and aromatherapy as examples,this paper discusses the application forms of digital therapeutics under the guidance of TCM theory,and provides a preliminary paradigm for TCM digital therapeutics.

Fenugreek is a kind of Chinese herbal medicine with great development prospects.At present,there are many modern reports on its components extraction,quality standards,processing technology and pharmacological effects.However,there is still a lack of research on sorting out and analyzing the relevant ancient records,and people's herbological cognition of fenugreek is still controversial and incomplete.In this study,through systematic review and analysis of ancient records,the names,original plant,processing methods and properties(including nature,flavor,meridian tropism,action,application,toxicity and contraindication)of fenugreek were comprehensively verified for the first time,which provided a herbological reference for further development,utilization and in-depth study of Fenugreek.It is found that fenugreek has several names such as Kudou,Luba,Huba and Jidou.Based on analyzing the descriptions and pictures of fenugreek original plant in ancient records,there is a strong likelihood that the ancient and modern medicinal fenugreek belong to the same species.In addition to the current common method of stir-frying with salt solution,the ancient books also recorded eight processing methods of fenugreek,such as stir-frying,steaming,baking,and calcining.According to the research of properties,it is found that fenugreek is warm-hot in nature,bitter,sweet,and pungent in flavor,and attributive to the kidney,stomach,liver and bladder meridians.In addition to the efficacy recorded in pharmacopoeia,this study also complemented fenugreek's actions of"improving hearing and eyesight,guiding fire to origin"and application of"fullness and discomfort in abdomen and hypochondrium".According to the textual research of toxicity and contraindication,fenugreek has been recorded as non-toxic in the past dynasties,but it should be used cautiously in the excess-heat syndrome,Yin deficiency syndrome,and pregnant women.