Computer Engineering and Applications ›› 2018, Vol. 54 ›› Issue (8): 260-264.DOI: 10.3778/j.issn.1002-8331.1709-0336

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Research on deep semantic features for disease-association relation extraction

KANG Xuqin1, WU Ou2, WANG Lei1, ZHANG Yin1, YANG Shuai1   

  1. 1.Institute of Health Service and Medical Information, Academy of Military Medical Sciences, Beijing 100850, China
    2.Center for Applied Mathematics & School of Mathematics, Tianjin University, Tianjin 300072, China
  • Online:2018-04-15 Published:2018-05-02


康旭琴1,吴  偶2,王  磊1,张  音1,杨  帅1   

  1. 1.军事医学科学院 卫生勤务与医学情报研究所,北京 100850
    2.天津大学 应用数学中心,天津 300072

Abstract: It is of great reference value for the research of prevention and cure of disease to find out the beneficial or harmful factors affecting the disease from a large number of biomedical literatures. However, it is difficult to identify the bottlenecks that are difficult to continue to improve when the accuracy of the classification is improved to a certain level by using the traditional machine learning method. In order to improve the performance of the classification task in biomedical field, the hybrid method of convolution neural network method and Support Vector Machine(SVM) is used by data with the two factors which are beneficial and harmful to disease. Ultimately, the method achieves better performance than the traditional machine learning, with the accuracy of classification from SVM increasing from 90.44% to 94.38%, so as to better identify the factors that affect the disease.

Key words: relation extraction, classification problem, deep learning, machine learning, convolutional neural network, Support Vector Machine(SVM)



关键词: 关联抽取, 分类问题, 深度学习, 机器学习, 卷积神经网络, 支持向量机