Abstract
Traditional Chinese Medicine (TCM) is a highly empirical, subjective and practical discipline. Generating an appropriate prescription has been one of the most crucial components in building intelligent diagnosis systems that provide clinical decision support to physicians. While various machine learning models for prescription generation have been created, they suffer from specific limitations (e.g., data complexity and semantic ambiguity, lack of syndrome differentiation thinking, etc.). For handling these limitations, we propose a novel Heterogeneous Graph Contrastive Learning (HGCL) based model to conduct prescription generation with the idea of syndrome differentiation and treatment. Specifically, we first model the TCM clinical prescriptions as a Heterogeneous Information Network (THIN), and then explore node- and semantic-level contrastive learning on THIN, so as to enhance the quality of node representations for several downstream tasks such as node classification, prescription generation, etc. We conduct extensive experiments on three real TCM clinical datasets, demonstrating significant improvement over state-of-the-art methods, even though some of which are fully unsupervised.
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Yin, Z., Wu, Y., Zhang, Y. (2022). HGCL: Heterogeneous Graph Contrastive Learning for Traditional Chinese Medicine Prescription Generation. In: Traina, A., Wang, H., Zhang, Y., Siuly, S., Zhou, R., Chen, L. (eds) Health Information Science. HIS 2022. Lecture Notes in Computer Science, vol 13705. Springer, Cham. https://doi.org/10.1007/978-3-031-20627-6_9
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