Multi-view Graph Representation Learning via Mutual Learning for Drug RecommendationDrug Recommendation via Multi-view Graph Learning
Authors: 
Liang Wang, Wushao Wen, Jinghui QinAuthors Info & Claims
Liang Wang, Wushao Wen, Jinghui QinAuthors Info & ClaimsPages 202 - 207
Abstract
Drug recommendation aims to recommend a combination of drugs to patients with specific symptoms based on the interactions between symptom sets and drug sets. However, most existing methods focus on the interaction between single symptom and single drug, ignoring the set-level semantic information of symptoms and drugs. Moreover, they usually rely on complex medical records (such as history treatment records, diagnosis, lab tests, and procedures) to learn the representation of symptoms and drugs. To address these issues, we propose Multi-view Graph Representation Learning via Mutual Learning for Drug Recommendation (MGRL-DR), a novel method that only requires the symptom condition of patients and can capture the item-level and set-level interaction between symptoms and drugs via collaborative learning. Our framework will construct three graphs: symptom-drug interaction graph, symptom set-drug interaction graph, and drug set-symptom interaction graph, and train three graph-based information extraction modules in a single stage to capture the item-level and set-level semantic information of symptoms and drugs simultaneously. Extensive experiments on benchmark datasets show the significant improvements of MGRL-DR over the state-of-the-art methods.
References
[1]
Tan, Y., Kong, C., Yu, L., Li, P., Chen, C., Zheng, X., Hertzberg, V.S. and Yang, C., 2022, August. 4sdrug: Symptom-based set-to-set small and safe drug recommendation. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 3970-3980). https://dl.acm.org/doi/10.1145/3534678.3539089
[2]
Yang, C., Xiao, C., Ma, F., Glass, L. and Sun, J., 2021. Safedrug: Dual molecular graph encoders for recommending effective and safe drug combinations. arXiv preprint arXiv:2105.02711. https://doi.org/10.24963/ijcai.2021/514
[3]
J. Wu, B. Qian, Y. Li, Z. Gao, M. Ju, Y. Yang, Y. Zheng, T. Gong, C. Li, X. Zhang, Leveraging multiple types of domain knowledge for safe and effective drug recommendation, in: M. A. Hasan, L. Xiong (Eds.), Proceedings of the 31st ACM International Conference on Information & Knowledge Management, Atlanta, GA, USA, October 17-21, 2022, ACM, 2022, pp. 2169–2178. URL https://doi.org/10.1145/3511808.3557380
[4]
He, X., Deng, K., Wang, X., Li, Y., Zhang, Y. and Wang, M., 2020, July. Lightgcn: Simplifying and powering graph convolution network for recommendation. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval (pp. 639-648). https://doi.org/10.1145/3397271.3401063
[5]
Rendle, S., Freudenthaler, C., Gantner, Z. and Schmidt-Thieme, L., 2012. BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618. https://doi.org/10.48550/arXiv.1205.2618
[6]
Kingma, D.P. and Ba, J., 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. https://doi.org/10.48550/arXiv.1412.6980
[7]
Zhang, Y., Xiang, T., Hospedales, T.M. and Lu, H., 2018. Deep mutual learning. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4320-4328). https://doi.org/10.1109/CVPR.2018.00454
[8]
Choi, E., Bahadori, M.T., Sun, J., Kulas, J., Schuetz, A. and Stewart, W., 2016. Retain: An interpretable predictive model for healthcare using reverse time attention mechanism. Advances in neural information processing systems, 29. https://proceedings.neurips.cc/paper_files/paper/2016/file/231141b34c82aa95e48810a9d1b33a79-Paper.pdf
[9]
Shang, J., Xiao, C., Ma, T., Li, H. and Sun, J., 2019, July. Gamenet: Graph augmented memory networks for recommending medication combination. In proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, No. 01, pp. 1126-1133). https://doi.org/10.1609/aaai.v33i01.33011126
[10]
Zhao, J., Luo, W., Chen, B. and Gilman, A., 2021, November. Mutual-learning improves end-to-end speech translation. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (pp. 3989-3994). http://dx.doi.org/10.18653/v1/2021.emnlp-main.325
[11]
Gou, J., Yu, B., Maybank, S.J. and Tao, D., 2021. Knowledge distillation: A survey. International Journal of Computer Vision, 129, pp.1789-1819. https://doi.org/10.1007/s11263-021-01453-z
[12]
Spiegel, M.R., 1965. Laplace transforms (p. 249). New York: McGraw-Hill.
[13]
Chai, T. and Draxler, R.R., 2014. Root mean square error (RMSE) or mean absolute error (MAE)?–Arguments against avoiding RMSE in the literature. Geoscientific model development, 7(3), pp.1247-1250. https://doi.org/10.5194/gmd-7-1247-2014
[14]
Johnson, A.E., Pollard, T.J., Shen, L., Lehman, L.W.H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Anthony Celi, L. and Mark, R.G., 2016. MIMIC-III, a freely accessible critical care database. Scientific data, 3(1), pp.1-9. https://doi.org/10.1038/sdata.2016.35
[15]
Wang, S., Ren, P., Chen, Z., Ren, Z., Ma, J. and de Rijke, M., 2019, November. Order-free medicine combination prediction with graph convolutional reinforcement learning. In Proceedings of the 28th ACM international conference on information and knowledge management (pp. 1623-1632). https://doi.org/10.1145/3357384.3357965
[16]
Read, J., Pfahringer, B., Holmes, G. and Frank, E., 2011. Classifier chains for multi-label classification. Machine learning, 85, pp.333-359. https://doi.org/10.1007/s10994-011-5256-5
[17]
Wu, R., Qiu, Z., Jiang, J., Qi, G. and Wu, X., 2022, April. Conditional generation net for medication recommendation. In Proceedings of the ACM Web Conference 2022 (pp. 935-945). https://doi.org/10.1145/3485447.3511936
[18]
Hinton, G., Vinyals, O. and Dean, J., 2015. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531. https://doi.org/10.48550/arXiv.1503.02531
[19]
Mao, A., Mohri, M. and Zhong, Y., 2023. Cross-entropy loss functions: Theoretical analysis and applications. arXiv preprint arXiv:2304.07288.
Index Terms
- Multi-view Graph Representation Learning via Mutual Learning for Drug RecommendationDrug Recommendation via Multi-view Graph Learning
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Published In
November 2023
902 pages
ISBN:9798400716485
DOI:10.1145/3653081
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Published: 03 May 2024
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- National Natural Science Foundation of China
- GuangDong Basic and Applied Basic Research Foundation
- China Postdoctoral Science Foundation
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IoTAAI 2023
IoTAAI 2023: 2023 5th International Conference on Internet of Things, Automation and Artificial Intelligence
November 24 - 26, 2023
Nanchang, China
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