{"type":"ScholarlyArticle","title":"Discovering Regularities from Traditional Chinese Medicine Prescriptions via Bipartite Embedding Model","authors":[{"name":"Chunyang Ruan","slug":"chunyang-ruan","given":"Chunyang","family":"Ruan","affiliation":"Fudan University , Shanghai , China; Zhejiang Lab , Hanghzou , China"},{"name":"Jiangang Ma","slug":"jiangang-ma","given":"Jiangang","family":"Ma","affiliation":"Federation University Australia , Melbourne , Australia"},{"name":"Ye Wang","slug":"ye-wang","given":"Ye","family":"Wang","affiliation":"Victoria University , Melbourne , Australia"},{"name":"Yanchun Zhang","slug":"yanchun-zhang","given":"Yanchun","family":"Zhang","affiliation":"Fudan University , Shanghai , China; Zhejiang Lab , Hanghzou , China; Victoria University , Melbourne , Australia"},{"name":"Yun Yang","slug":"yun-yang","given":"Yun","family":"Yang","affiliation":"Longhua Hospital Shanghai University of Traditional Chinese Medicine , Shanghai , China"}],"abstract":"Regularities analysis for prescriptions is a significant task for traditional Chinese medicine (TCM), both in inheritance of clinical experience and in improvement of clinical quality. Recently, many methods have been proposed for regularities discovery, but this task is challenging due to the quantity, sparsity and free-style of prescriptions. In this paper, we address the specific problem of regularities discovery and propose a graph embedding based framework for regularities discovery for massive prescriptions. We model this task as a relation prediction in which the correlation of two herbs or of herb and symptom are incorporated to characterize the different relationships. Specifically, we first establish a heterogeneous network with herbs and symptoms as its nodes. We develop a bipartite embedding model termed HS2Vec to detect regularities, which explores multiple relations of herbherb, and herb-symptom based on the heterogeneous network. Experiments on four real-world datasets demonstrate that the proposed framework is very effective for regularities discovery.","doi":"10.24963/ijcai.2019/464","url":"https://ijcai.org/proceedings/2019/464","pdf":"https://ijcai.org/proceedings/2019/0464.pdf","markdown":"https://ijcai.org/proceedings/2019/464.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/464","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning M-Z","track":"Main track","number":464,"pages":{"first":3346,"last":3352},"keywords":["Machine Learning: Unsupervised Learning","Machine Learning Applications: Bio;Medicine","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Chen et al., 2018] Xintian Chen, Chunyang Ruan, Yanchun Zhang, and Huijuan Chen. 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