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Prescription Recommendation Algorithm Based on Herbal Property-Driven Compatibility Mechanism Semantic Modeling
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Xueru GENG1, Jiantong ZHANG1, Jianchen HOU2, Xiaohua TAO2, Tao LUO1
Journal of Beijing University of Posts and Telecommunications | 2025, 48(5) : 128 - 135
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Journal of Beijing University of Posts and Telecommunications | 2025, 48(5): 128-135
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Prescription Recommendation Algorithm Based on Herbal Property-Driven Compatibility Mechanism Semantic Modeling
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Xueru GENG1, Jiantong ZHANG1, Jianchen HOU2, Xiaohua TAO2, Tao LUO1
Affiliations
  • 1.School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • 2.School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 102488, China
doi: 10.13190/j.jbupt.2025-001
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The in-depth analysis of the semantic information contained in traditional Chinese medicine (TCM) prescriptions is of great significance for both clinical applications and the discovery of new formulas. Existing TCM prescription generation algorithms define the interactions between all symptom herb pairs solely based on co-occurrence, without considering the categorization of herbal properties. To address this issue, this paper proposes a prescription recommendation algorithm based on herbal property driven compatibility mechanism semantic modeling (HPDCM). First, the analysis of prescriptions takes into account the herbal property categories, which are defined as entities when constructing the knowledge graph (KG). Second, the algorithm integrates compatibility rules to model the interactions between symptoms and herbs with weighted connections. This is followed by aggregating higher-order heterogeneous path information of nodes through a graph convolutional network (GCN) model. Finally, an attention mechanism is employed to fuse information from symptom interaction graphs, symptom-herb interaction graphs, and herb interaction graphs, distinguishing the influence of different dimensions of TCM semantic information. Experimental results, compared with existing formula generation algorithms, demonstrate that HPDCM achieves higher accuracy and is more in line with the TCM diagnostic and therapeutic principles of syndrome differentiation and treatment.

traditional Chinese medicine prescriptions  /  traditional Chinese medicine diagnosis and treatment  /  knowledge graph  /  graph convolutional network  /  attention mechanism
Xueru GENG, Jiantong ZHANG, Jianchen HOU, Xiaohua TAO, Tao LUO. Prescription Recommendation Algorithm Based on Herbal Property-Driven Compatibility Mechanism Semantic Modeling[J]. Journal of Beijing University of Posts and Telecommunications, 2025 , 48 (5) : 128 -135 . DOI: 10.13190/j.jbupt.2025-001
Year 2025 volume 48 Issue 5
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doi: 10.13190/j.jbupt.2025-001
  • Receive Date:2025-01-03
  • Online Date:2026-04-16
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  • Received:2025-01-03
Affiliations
    1.School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
    2.School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 102488, China
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表12种不同金属材料的力学参数

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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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