- Title
- Discovering regularities from traditional chinese medicine prescriptions via bipartite embedding model
- Creator
- Ruan, Chunyang; Ma, Jiangang; Wang, Ye; Zhang, Yanchun; Yang, Yun
- Date
- 2019
- Type
- Text; Conference proceedings
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/169805
- Identifier
- vital:14100
- Identifier
-
https://doi.org/10.24963/ijcai.2019/464
- Identifier
- ISBN:978-0-9992411-4-1
- 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.
- Publisher
- International Joint Conferences on Artificial Intelligence
- Relation
- International Joint Conferences on Artificial Intelligence (IJCAI-49); Macao, China; 10th-16th August 2019; published in Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19) p. 3346-3352
- Rights
- Copyright © 2019 International Joint Conferences on Artificial Intelligence. All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher.
- Rights
- Open Access
- Rights
- This metadata is freely available under a CCO license
- Subject
- Machine learning; Data mining; Unsupervised learning; Machine learning applications; Biomedicine
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