Semantic-Aware Graph Convolutional Networks for Clinical Auxiliary Diagnosis and Treatment of Traditional Chinese Medicine
Traditional Chinese Medicine (TCM) clinical informatization focuses on serving user-oriented health knowledge and facilitating online diagnosis. Regularities are hidden in clinical knowledge play a significant role in the improvement of the TCM informatization service. However, many regularities can...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9312604/ |
| _version_ | 1828112037898616832 |
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| author | Chunyang Ruan Yingpei Wu Yun Yang Guangsheng Luo |
| author_facet | Chunyang Ruan Yingpei Wu Yun Yang Guangsheng Luo |
| author_sort | Chunyang Ruan |
| collection | DOAJ |
| description | Traditional Chinese Medicine (TCM) clinical informatization focuses on serving user-oriented health knowledge and facilitating online diagnosis. Regularities are hidden in clinical knowledge play a significant role in the improvement of the TCM informatization service. However, many regularities can hardly be discovered because of specific data-challenges in TCM prescriptions at present. Therefore, in this article, we propose an end-to-end model, called Semantic-aware Graph Convolutional Networks (SaGCN) model, to learn the latent regularities in three steps: (1) We first construct a heterogeneous graph based on prescriptions; (2) We stack Semantic-aware graph convolution to learn effective low-dimensional representations of nodes by meta-graphs and self-attention; (3) With the learned representations, we can detect regularities accurately by clustering and linked prediction. To the best of our knowledge, this is the first study to use metagraph and graph convolutional networks for modeling TCM clinical data and diagnosis prediction. Experimental results on three real datasets demonstrate SaGCN outperforms the state-of-the-art models for clinical auxiliary diagnosis and treatment. |
| first_indexed | 2024-04-11T11:45:24Z |
| format | Article |
| id | doaj.art-5d4b99b6ba374135b6979ffe44a3a818 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T11:45:24Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-5d4b99b6ba374135b6979ffe44a3a8182022-12-22T04:25:38ZengIEEEIEEE Access2169-35362021-01-0198797880710.1109/ACCESS.2020.30489329312604Semantic-Aware Graph Convolutional Networks for Clinical Auxiliary Diagnosis and Treatment of Traditional Chinese MedicineChunyang Ruan0https://orcid.org/0000-0002-6280-0148Yingpei Wu1Yun Yang2Guangsheng Luo3https://orcid.org/0000-0003-4278-2971Department of Data Science and Big Data Technology, School of Economics and Finance, Shanghai International Studies University, Shanghai, ChinaSchool of Software Engineering, Fudan University, Shanghai, ChinaDepartment of Oncology, Longhua Hospital Shanghai University of Traditional Chinese Medicine, Shanghai, ChinaDepartment of Data Science and Big Data Technology, School of Economics and Finance, Shanghai International Studies University, Shanghai, ChinaTraditional Chinese Medicine (TCM) clinical informatization focuses on serving user-oriented health knowledge and facilitating online diagnosis. Regularities are hidden in clinical knowledge play a significant role in the improvement of the TCM informatization service. However, many regularities can hardly be discovered because of specific data-challenges in TCM prescriptions at present. Therefore, in this article, we propose an end-to-end model, called Semantic-aware Graph Convolutional Networks (SaGCN) model, to learn the latent regularities in three steps: (1) We first construct a heterogeneous graph based on prescriptions; (2) We stack Semantic-aware graph convolution to learn effective low-dimensional representations of nodes by meta-graphs and self-attention; (3) With the learned representations, we can detect regularities accurately by clustering and linked prediction. To the best of our knowledge, this is the first study to use metagraph and graph convolutional networks for modeling TCM clinical data and diagnosis prediction. Experimental results on three real datasets demonstrate SaGCN outperforms the state-of-the-art models for clinical auxiliary diagnosis and treatment.https://ieeexplore.ieee.org/document/9312604/Tranditional Chinese medicineclinical knowledge discoverymetagraphgraph convolutional networks |
| spellingShingle | Chunyang Ruan Yingpei Wu Yun Yang Guangsheng Luo Semantic-Aware Graph Convolutional Networks for Clinical Auxiliary Diagnosis and Treatment of Traditional Chinese Medicine IEEE Access Tranditional Chinese medicine clinical knowledge discovery metagraph graph convolutional networks |
| title | Semantic-Aware Graph Convolutional Networks for Clinical Auxiliary Diagnosis and Treatment of Traditional Chinese Medicine |
| title_full | Semantic-Aware Graph Convolutional Networks for Clinical Auxiliary Diagnosis and Treatment of Traditional Chinese Medicine |
| title_fullStr | Semantic-Aware Graph Convolutional Networks for Clinical Auxiliary Diagnosis and Treatment of Traditional Chinese Medicine |
| title_full_unstemmed | Semantic-Aware Graph Convolutional Networks for Clinical Auxiliary Diagnosis and Treatment of Traditional Chinese Medicine |
| title_short | Semantic-Aware Graph Convolutional Networks for Clinical Auxiliary Diagnosis and Treatment of Traditional Chinese Medicine |
| title_sort | semantic aware graph convolutional networks for clinical auxiliary diagnosis and treatment of traditional chinese medicine |
| topic | Tranditional Chinese medicine clinical knowledge discovery metagraph graph convolutional networks |
| url | https://ieeexplore.ieee.org/document/9312604/ |
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