Earticle

초록

영어

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.

목차

Abstract
 1. Introduction
 2. Related Works
 3. Relation Scheme and Data Sets
 4. Methodologies
  4.1. Preprocessing and Features Extraction
  4.2. Features Optimization with Deep Learning
 5. Results and Analysis
 6. Conclusions and Future Work
 References

키워드

저자

• Xinbo Lv [ School of Computer Science and Technology. Harbin Institute of Technology, Harbin, 150001, P.R. China ]
• Yi Guan [ School of Computer Science and Technology. Harbin Institute of Technology, Harbin, 150001, P.R. China ]
• Jinfeng Yang [ School of Software. Harbin University of Science and Technology, Harbin, 150080, P.R. China ]
• Jiawei Wu [ School of Computer Science and Technology. Harbin Institute of Technology, Harbin, 150001, P.R. China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605