Abstract
Regularities of prescriptions are important for both clinical practice and novel healthcare development in clinical traditional Chinese medicine (TCM). To address this issue and meet clinical demand for determining treatments, we propose an unsupervised analysis model termed AMNE to determine effective herbs for diverse symptoms in prescriptions. Results confirmed by human physicians demonstrate AMNE can outperform several previous TCM regularity discovery models in prescriptions.
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CITATION STYLE
Ruan, C., Wang, Y., Zhang, Y., & Yang, Y. (2019). Exploring Regularity in Traditional Chinese Medicine Clinical Data Using Heterogeneous Weighted Networks Embedding. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11448 LNCS, pp. 310–313). Springer Verlag. https://doi.org/10.1007/978-3-030-18590-9_35
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