Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/IJCNN52387.2021.9534468 | 2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) |
Keywords | DocType | ISSN |
recommender system, herb recommendation, graph convolutional network, traditional chinese medicine | Conference | 2161-4393 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yinghong Zhang | 1 | 0 | 0.68 |
| Song Liu | 2 | 0 | 0.68 |
| Jianhui Xie | 3 | 0 | 0.68 |
| Ruixing Liu | 4 | 0 | 0.34 |
| Zhu Yuesheng | 5 | 112 | 39.21 |
| Zhiqiang Bai | 6 | 1 | 1.71 |