文章基本信息

标题：Clinical Relation Extraction with Deep Learning
本地全文：下载
作者：Xinbo Lv ; Yi Guan ; Jinfeng Yang 等
期刊名称：International Journal of Hybrid Information Technology
印刷版ISSN：1738-9968
出版年度：2016
卷号：9
期号：7
页码：237-248
DOI：10.14257/ijhit.2016.9.7.22
出版社：SERSC
摘要：Relations between medical concepts convey meaningful medical knowledge and patients’ health information. Relation extraction on Clinical texts is an important task of information extraction in clinical domain, and is the key step of building medical knowledge graph. In this research, the task of relation extraction is based on the task of concept recognition and is implemented as relation classification by the adoption of a CRF model. The proposed CRF-powered classification model depends on features of context of concepts. To remedy the problem of word sparsity, a deep learning model is applied for features optimization by the employment of auto encoder and sparsity limitation. The proposed model is validated on the data set of I2B2 2010. The experiments give the evidence that the proposed model is effective and the method of features optimization with the deep learning model shows the great potential.
关键词：relation extraction; clinical narrative; deep learning; auto encoder; sparsity limitation