โก Quick Summary
This study introduces a novel framework, SD-MKEK, for syndrome differentiation (SD) in Traditional Chinese Medicine (TCM) by leveraging the Kolmogorov-Arnold Theorem. The framework demonstrates superior performance in accurately identifying TCM syndromes compared to existing methods, enhancing both accuracy and interpretability.
๐ Key Details
- ๐ Datasets: Multi-disease multi-syndrome TCM-SD dataset and single-disease multi-syndrome COPD-SD dataset
- ๐งฉ Features used: Patient clinical information, symptoms, and signs
- โ๏ธ Technology: SD-MKEK framework combining multiple knowledge enhancement and Kolmogorov-Arnold classifier
- ๐ Performance: SD-MKEK outperforms state-of-the-art baselines in syndrome differentiation tasks
๐ Key Takeaways
- ๐ก Syndrome differentiation is crucial for effective diagnosis and treatment in TCM.
- ๐ค SD-MKEK utilizes a hierarchical structure to capture complex relationships between symptoms and syndromes.
- ๐ Multi knowledge enhancement significantly improves feature extraction and discriminability.
- ๐ Experimental results indicate that SD-MKEK surpasses existing methods in accuracy and interpretability.
- ๐ This study promotes the integration of traditional medicine with modern computing technologies.
- ๐ง The framework’s design emphasizes the importance of non-linear feature modeling.
- ๐ Effective in distinguishing rare syndrome types, enhancing diagnostic precision.
๐ Background
Traditional Chinese Medicine (TCM) has been a cornerstone of healthcare practices for centuries, emphasizing a holistic approach to diagnosis and treatment. A critical aspect of TCM is syndrome differentiation (SD), which involves analyzing a patient’s clinical information to map symptoms to specific syndrome types. However, the complexity of this process necessitates advanced modeling techniques that can handle non-linear relationships and semantic associations between various symptoms and syndromes.
๐๏ธ Study
The study proposes the SD-MKEK framework, which integrates the Kolmogorov-Arnold Theorem to enhance the syndrome differentiation process in TCM. By employing a multi knowledge enhancement approach, the framework aims to improve the accuracy and interpretability of syndrome identification. The research utilized two datasets: a multi-disease multi-syndrome TCM-SD dataset and a single-disease multi-syndrome COPD-SD dataset, to validate the effectiveness of the proposed method.
๐ Results
The experimental results demonstrated that the SD-MKEK framework significantly outperformed existing state-of-the-art baselines in both datasets. The framework’s ability to effectively capture complex relationships and enhance feature discriminability led to improved accuracy in syndrome differentiation tasks. This indicates a promising advancement in the application of computational techniques to traditional medical practices.
๐ Impact and Implications
The findings of this study have profound implications for the future of TCM and its integration with modern healthcare technologies. By enhancing the accuracy of syndrome differentiation, the SD-MKEK framework can lead to better patient outcomes and more personalized treatment plans. This research not only highlights the potential of advanced computational methods in traditional medicine but also paves the way for further innovations in the field.
๐ฎ Conclusion
The introduction of the SD-MKEK framework marks a significant step forward in the realm of Traditional Chinese Medicine. By effectively combining traditional practices with modern computational techniques, this study showcases the potential for improved diagnostic accuracy and patient care. As we continue to explore the intersection of technology and medicine, the future looks promising for the integration of AI in healthcare.
๐ฌ Your comments
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Syndrome differentiation of Traditional Chinese Medicine via multiple knowledge enhancement with Kolmogorov-Arnold Theorem.
Abstract
Traditional Chinese Medicine (TCM) plays an important role in global medical practices. Syndrome differentiation (SD) is a key step in the diagnosis and treatment of TCM, which involves a comprehensive analysis of patient clinical information. However, the process of SD involves a complex mapping of various symptoms and signs to their corresponding syndrome types. It requires models to have strong non-linear feature modeling capabilities, emphasizing the semantic associations and feature differences between syndrome types. Additionally, the models must be able to effectively distinguish rare syndrome types, thereby enhancing both accuracy and interpretability. To this end, a multi knowledge enhanced framework combined with Kolmogorov-Arnold, named SD-MKEK, is proposed. SD-MKEK effectively captures the complex relationships between syndrome types and symptoms through a hierarchical structure, enabling accurate SD. In the feature extraction phase, multiple knowledge enhancement module is designed to extract context-sensitive features and significantly enhance the discriminability of the features through a label-guided mechanism. In the decision-making phase, a cross-attention mechanism is combined with the Kolmogorov-Arnold classifier, and a learnable activation function is used to better capture the complex relationships in high-dimensional data. Experimental results on the multi-disease multi-syndrome TCM-SD dataset show that the performance of SD-MKEK is superior to existing state-of-the-art baselines. Experiments on the single-disease multi-syndrome COPD-SD dataset also demonstrate the effectiveness of the proposed algorithm. This study can effectively perform the task of identifying TCM syndromes and has important value in promoting the deep integration of traditional medicine with modern computing technologies.
Author: [‘Yang Y’, ‘Lu X’, ‘An W’, ‘Wei H’, ‘Li X’, ‘Wang P’, ‘Wei B’]
Journal: Artif Intell Med
Citation: Yang Y, et al. Syndrome differentiation of Traditional Chinese Medicine via multiple knowledge enhancement with Kolmogorov-Arnold Theorem. Syndrome differentiation of Traditional Chinese Medicine via multiple knowledge enhancement with Kolmogorov-Arnold Theorem. 2026; 176:103396. doi: 10.1016/j.artmed.2026.103396