AI IN HEALTHCARE
A Hierarchical Structure-Enhanced Personalized Recommendation Model for Traditional Chinese Medicine Formulas Based on KG Diffusion Guidance
Artificial intelligence (AI) technology plays a crucial role in recommending prescriptions for traditional Chinese medicine (TCM). This paper proposes a novel hierarchical structure-enhanced personalized recommendation model for TCM formulas based on knowledge graph (KG) diffusion guidance, namely TCM-HEDPR. It addresses limitations in existing models such as insufficient attention to patient-personalized information, long-tailed distribution of herb data, and oversight of herb compatibility. The model pre-trains symptom representations using patient-personalized prompt sequences and applies prompt-oriented contrastive learning for data augmentation. A KG-guided homogeneous graph diffusion method with self-attention globally captures non-linear symptom-herb relationships. A heterogeneous graph hierarchical network integrates herbal dispensing relationships with implicit syndromes. Extensive experiments on public and clinical datasets demonstrate its effectiveness, providing a new paradigm for modern TCM recommendation.
Executive Impact
This research introduces TCM-HEDPR, an AI model significantly advancing personalized Traditional Chinese Medicine (TCM) prescription recommendations. By integrating patient-specific data, leveraging knowledge graphs, and incorporating TCM's unique 'monarch, minister, assistant, and envoy' herb compatibility principles, TCM-HEDPR offers superior accuracy and interpretability. This innovation allows healthcare providers to offer more precise, safer, and tailored herbal treatments, mitigating risks associated with traditional methods and significantly improving patient outcomes. It sets a new standard for AI application in personalized medicine within complex, data-rich environments.
The TCM-HEDPR model addresses critical limitations in existing AI-driven TCM prescription systems. It overcomes challenges like the scarcity of personalized patient data through prompt-oriented contrastive learning and pre-embedding techniques. The model mitigates biases from long-tailed herb data distribution using a heterogeneous graph hierarchical network. Crucially, it integrates the complex 'monarch, minister, assistant, and envoy' herb compatibility relationships, which were previously overlooked, reducing toxicity risks and aligning with clinical TCM principles. Its use of a knowledge graph diffusion method accurately models intricate symptom-herb relationships. Validated across multiple datasets, TCM-HEDPR demonstrates robust performance and a highly interpretable framework, essential for clinical decision-making and the modernization of TCM practices.
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Performance Improvement
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Accuracy (Clinical Data)
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Entities in TCM_IKG
Deep Analysis & Enterprise Applications
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Model Architecture
Personalization & Data Augmentation
Knowledge Graph & Diffusion
Herb Compatibility & Hierarchy
TCM-HEDPR features a multi-module design including a Patient Individualized Feature Pre-embedding Module (PEPP), Diffusion-Guided Symptom-Herb Representation Learning Module (DMSH), Syndrome-Aware Prediction Module (SYN), and a Heterogeneous Graph-Enhanced Hierarchical Structured Network of Herbs (HGSN). These components collectively address personalization, data sparsity, and complex herb interaction modeling.
The PEPP module employs prompt sequences and contrastive learning to pre-embed personalized patient attributes, effectively augmenting data and capturing complex symptom-herb relationships. This addresses the challenge of scarce patient data and enhances the model's ability to provide tailored recommendations.
The DMSH module utilizes a knowledge graph (TCM_IKG) and a diffusion probability model to capture higher-order semantic relationships between symptoms and herbs. This integration enriches entity relationships and synergistically models interactions, overcoming limitations of fixed-dimensional embeddings in previous models.
The HGSN module is a novel hierarchical network that explicitly models the 'monarch, minister, assistant, and envoy' relationships among herbs. This structured approach, combined with the SYN module, guides prescription generation at a fine-grained level, mitigates long-tailed herb distribution, and reduces toxicity risks, aligning with core TCM principles.
12.37%
Performance Improvement over SMRGAT
TCM-HEDPR shows significant performance gains in terms of NDCG@20 over state-of-the-art methods like SMRGAT on the TCM_KMGD dataset.
Enterprise Process Flow
Patient Characteristics & Symptom Collection
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Patient Individualized Feature Pre-embedding
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KG-Guided Symptom-Herb Representation Learning
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Syndrome-Aware Prediction & Herb Compatibility
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Personalized Herb Recommendation
| Feature | Our Approach Benefits | Traditional Approach Limitations |
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| Patient Personalization |
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| Herb Data Distribution |
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| Herb Compatibility |
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Clinical Efficacy Validation
The TCM-HEDPR model was validated on a clinical dataset (TCM_KMGD), demonstrating high accuracy in recommending prescriptions that align with real-world TCM practice.
Challenge: Accurately replicating clinical diagnostic and treatment processes in TCM, especially for complex cases involving multiple symptoms and personalized patient data.
Solution: TCM-HEDPR leverages personalized patient attributes, a comprehensive TCM knowledge graph, and a hierarchical understanding of herb compatibility to mimic the holistic approach of an experienced TCM doctor.
Outcome: Achieved 80% accuracy and high hit rate (NDCG) in recommending herbs that correspond with real prescriptions, providing valuable support for clinical diagnosis and decision-making.
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