Abstract
The Traditional Chinese Medicine Health Status Identification plays an important role in TCM diagnosis and prescription recommendation. In this paper, we propose a method of Status Identification via Graph Attention Network, named SIGAT, which captures the complex medical correlation in the symptom-syndrome graph. More specifically, we construct a symptom-syndrome graph in that symptoms are taken as nodes and the edges are connected by syndromes. And we realize automatic induction of symptom to state element classification by using the attention mechanism and perceptron classifier. Finally, we conduct experiments by using hamming loss, coverage, 0/1 error, ranking loss, average precision, macro-F1 score, and micro-F1 score as evaluation metrics. The results demonstrate that the SIGAT model outperforms comparison algorithms on Traditional Chinese Medicine Prescription Dictionary dataset. The case study results suggest that the proposed method is a valuable way to identify the state element. The application of the graph attention network classification algorithm in TCM health status identification is of high precision and methodological feasibility.
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References
Chen, S., Wang, Y.: Analysis of TCM health management mode with status identification as core. Asia-Pacific Tradit. Med. 15(11), 165â166 (2019)
Fan, H., Zhang, F., Wang, R., Huang, X., Li, Z.: Semi-supervised time series classification by temporal relation prediction. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 3545â3549 (2021)
Fan, H., et al.: Heterogeneous hypergraph variational autoencoder for link prediction. IEEE Trans. Pattern Anal. Mach. Intell. 44(8), 4125â4138 (2021). https://doi.org/10.1109/TPAMI.2021.3059313
Fan, H., Zhang, F., Xi, L., Li, Z., Guanghai, L., Xu, Y.: LeukocyteMask: an automated localization and segmentation method for leukocyte in blood smear images using deep neural networks. J. Biophotonicse 12(7) (2019)
Feng, S.: Classical prescriptions syndrome differentiation based on symptomatic response. China J. Tradit. Chinese Med. Pharm. 36(1), 22â26 (2021)
Li, C., Yang, X., Gan, H., Lai, X., Changen, Z., Chen, M.: China journal of traditional Chinese medicine and pharmacy. Asia-Pacific Tradit. Med. 26(6), 1351â1355 (2011)
Liang, W., Lin, X., Yu, J., Min, L., Li, C.: Big database of real world promotes health management of traditional Chinese medicine into artificial intelligence era. China J. Tradit. Chinese Med. Pharm. 33(4), 1213â1215 (2018)
Liu, J., Xie, Y.: Collaborative filtering recommendation algorithm based on graph attention network representation learning. Comput. Syst. Appl. 31(4), 273â280 (2022)
Nie, T.: Research on short text classification based on graph attention networks. Northeast Normal University (2021)
Pan, M.: Research on social recommender system based on graph attention network. Northeast Normal University (2021)
Shi, Y., Zhou, K., Li, S., Zhou, M., Liu, W.: Heterogeneous graph attention network for food safety risk prediction. J. Food Eng. 323, 111005 (2022)
Song, Z., Li, Y., Li, D., Li, S.: Multi-label classification of legal text with fusion of label relations. Pattern Recogn. Artif. Intell. 35(2), 185â192 (2022)
Tsoumakas, G., Katakis, I., Vlahavas, I.: Mining multi-label data. In: Data Mining and Knowledge Discovery Handbook, pp. 667â685 (2010)
VeliÄkoviÄ, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. In: Proceedings of the International Conference on Learning Representations (2017)
Xin, J.: Research of health status identification algorithm based on TCM theory of state. Fujian University of Traditional Chinese Medicine (2021)
Xin, J., Li, S., Zhang, J., Lei, H., Candong, L.: Dicovery of identification method of traditional Chinese medicine health status. China J. Tradit. Chinese Med. Pharm. 34(7), 3151â3153 (2019)
Xin, J., Zhang, J., Li, S., Li, C.: Research on multi-label classification methods for the identification of health state in traditional Chinese medicine. China J. Tradit. Chinese Med. Pharm. 34(9), 3952â3955 (2019)
Xu, J., et al.: Rules of traditional Chinese medicine state identification based on artificial intelligence algorithm. J. Tradit. Chinese Med. 61(3), 204â208 (2020)
Zhang, M., Wu, L.: Lift: multi-label learning with label-specific features. IEEE Trans. Pattern Anal. Mach. Intell. 37(1), 107â120 (2015)
Zhao, W., Lu, W., Li, Z., Zhou, C., Fan, H., Yang, Z.: TCM herbal prescription recommendation model based on multi-graph convolutional network. J. Ethnopharmacol. 297, 115109 (2022)
Acknowledgments
This work is partially supported by National Natural Science Foundation of China (61972187), Natural Science Foundation of Fujian Province (2020J02024).
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Fu, A., Ma, J., Wang, C., Zhou, C., Li, Z., Teng, S. (2023). Traditional Chinese Medicine Health Status Identification with Graph Attention Network. In: Xu, Y., Yan, H., Teng, H., Cai, J., Li, J. (eds) Machine Learning for Cyber Security. ML4CS 2022. Lecture Notes in Computer Science, vol 13655. Springer, Cham. https://doi.org/10.1007/978-3-031-20096-0_1
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