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 f...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 54152 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.展开更多
Predicting concrete compressive strength with limited data remains a critical challenge in civil engineering.This study proposes a novel framework integrating Model-Agnostic Meta-Learning(MAML)with SHAP(Shapley Additi...Predicting concrete compressive strength with limited data remains a critical challenge in civil engineering.This study proposes a novel framework integrating Model-Agnostic Meta-Learning(MAML)with SHAP(Shapley Additive Explanations)to improve predictive accuracy and interpretability in data-scarce scenarios.Unlike conventional machine learning models that require extensive data,the MAML-based approach enables rapid adaptation to new tasks using minimal samples,offering robust generalization in few-shot learning contexts.The proposed pipeline includes structured preprocessing,normalization,a neural network-based meta-learning core,and SHAP-based feature attribution.A curated dataset of 430 samples was used,focusing on 28-day compressive strength,with input features including cement,water,aggregates,admixtures,and age.Compared to standard models like XGBoost and Random Forest,the MAML framework achieved superior performance,with MAE=3.56 MPa,RMSE=5.55 MPa,and R^(2)=0.913.SHAP analysis revealed nonlinear interactions and dominant factors like water-cement ratio,curing age,and aggregate content.Statistical validation via the Wilcoxon Signed-Rank Test confirmed the significance of the model’s improvements(p<0.05).Furthermore,SHAP insights closely align with domain knowledge and mix design principles,enhancing model transparency for practical application.This work demonstrates the applicability of meta-learning in civil engineering and provides a scalable,interpretable solution for strength prediction in real-world,data-limited conditions.展开更多
基金Sichuan TCM Culture Coordinated Development Research Center Project(2023XT131)National Key Science and Technology Project of China(2023ZD0509405)National Natural Science Foundation of China(82174236).
文摘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 54152 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.
文摘Predicting concrete compressive strength with limited data remains a critical challenge in civil engineering.This study proposes a novel framework integrating Model-Agnostic Meta-Learning(MAML)with SHAP(Shapley Additive Explanations)to improve predictive accuracy and interpretability in data-scarce scenarios.Unlike conventional machine learning models that require extensive data,the MAML-based approach enables rapid adaptation to new tasks using minimal samples,offering robust generalization in few-shot learning contexts.The proposed pipeline includes structured preprocessing,normalization,a neural network-based meta-learning core,and SHAP-based feature attribution.A curated dataset of 430 samples was used,focusing on 28-day compressive strength,with input features including cement,water,aggregates,admixtures,and age.Compared to standard models like XGBoost and Random Forest,the MAML framework achieved superior performance,with MAE=3.56 MPa,RMSE=5.55 MPa,and R^(2)=0.913.SHAP analysis revealed nonlinear interactions and dominant factors like water-cement ratio,curing age,and aggregate content.Statistical validation via the Wilcoxon Signed-Rank Test confirmed the significance of the model’s improvements(p<0.05).Furthermore,SHAP insights closely align with domain knowledge and mix design principles,enhancing model transparency for practical application.This work demonstrates the applicability of meta-learning in civil engineering and provides a scalable,interpretable solution for strength prediction in real-world,data-limited conditions.
