Chemical-induced disease extraction via recurrent piecewise convolutional neural networks.

Journal: BMC medical informatics and decision making
Published Date: Jul 23, 2018

Abstract

BACKGROUND: Extracting relationships between chemicals and diseases from unstructured literature have attracted plenty of attention since the relationships are very useful for a large number of biomedical applications such as drug repositioning and pharmacovigilance. A number of machine learning methods have been proposed for chemical-induced disease (CID) extraction due to some publicly available annotated corpora. Most of them suffer from time-consuming feature engineering except deep learning methods. In this paper, we propose a novel document-level deep learning method, called recurrent piecewise convolutional neural networks (RPCNN), for CID extraction.

Authors

  • Haodi Li
    Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen 518055, China. Electronic address: haodili.hit@gmail.com.
  • Ming Yang
    Wuhan Institute for Food and Cosmetic Control, Wuhan 430014, China.
  • Qingcai Chen
    Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China.
  • Buzhou Tang
  • Xiaolong Wang
    Cardiovascular Department, Shuguang Hospital Affiliated to Shanghai University of TCM Shanghai, China.
  • Jun Yan
    Department of Statistics, University of Connecticut, Storrs, CT 06269, USA.

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