计算机科学
嵌入
同种类的
个性化
图形
语义鸿沟
注意力网络
人工智能
自然语言处理
理论计算机科学
数学
图像检索
组合数学
图像(数学)
万维网
作者
Yinghong Zhang,Song Liu,Jianhui Xie,Ruixing Liu,Yuesheng Zhu,Zhiqiang Bai
出处
期刊:International Joint Conference on Neural Network
日期:2021-07-18
标识
DOI:10.1109/ijcnn52387.2021.9534468
摘要
The herb recommendation system aiming for recommending a set of herb for patients is a significant task for Traditional Chinese Medicine (TCM). Recent works apply a graph convolutional network to model the relations among symptoms and herbs, showing promising performance. However, they typically suffer from two limitations: (1) The learning of the relations of symptoms and herbs from symptom-herb heterogeneous graphs would be disturbed by the semantic gap and the weak correlations between symptoms and herbs. (2) They ignore the complex diagnosis and systemic relations of a patient's multi-symptom, resulting in the lack of effectiveness and personalization in syndrome diagnosis. To overcome these limitations, we propose a novel Homogeneous Symptom Graph Attentive Reasoning Network (HSGARN). Firstly, to alleviate the noisy semantic gap and weak correlations of heterogeneous graphs, we propose a homogeneous graph embedding module to comprehensively model the semantic relations of symptoms and herbs. Secondly, we propose a symptom attentive reasoning module to generate syndrome representation for patients, which can sufficiently exploit the interrelation of a patient's symptoms and model the individual difference. Experimental results on two TCM datasets demonstrate the advantages of HSGARN over the state-of-the-arts.
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