Time-sensitive clinical concept embeddings learned from large electronic health records

Time-sensitive clinical concept embeddings learned from large electronic health records

Abstract Background Learning distributional representation of clinical concepts (e.g., diseases, drugs, and labs) is an important research area of deep learning in the medical domain. However, many existing relevant methods do not consider temporal dependencies along the longitudinal sequence of a p...

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Bibliographic Details
Main Authors: Yang Xiang, Jun Xu, Yuqi Si, Zhiheng Li, Laila Rasmy, Yujia Zhou, Firat Tiryaki, Fang Li, Yaoyun Zhang, Yonghui Wu, Xiaoqian Jiang, Wenjin Jim Zheng, Degui Zhi, Cui Tao, Hua Xu
Format: Article
Language:English
Published: BMC 2019-04-01
Series:BMC Medical Informatics and Decision Making
Subjects:
Clinical concept embedding
Distributional representation
Time sensitive concept embedding
Electronic medical records
Concept similarity
Predictive modeling
Online Access:http://link.springer.com/article/10.1186/s12911-019-0766-3

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http://link.springer.com/article/10.1186/s12911-019-0766-3

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