Entity recognition from clinical texts via recurrent neural network.

Journal: BMC medical informatics and decision making
Published Date: Jul 5, 2017

Abstract

BACKGROUND: Entity recognition is one of the most primary steps for text analysis and has long attracted considerable attention from researchers. In the clinical domain, various types of entities, such as clinical entities and protected health information (PHI), widely exist in clinical texts. Recognizing these entities has become a hot topic in clinical natural language processing (NLP), and a large number of traditional machine learning methods, such as support vector machine and conditional random field, have been deployed to recognize entities from clinical texts in the past few years. In recent years, recurrent neural network (RNN), one of deep learning methods that has shown great potential on many problems including named entity recognition, also has been gradually used for entity recognition from clinical texts.

Authors

  • Zengjian Liu
    Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen 518055, China. Electronic address: liuzengjian.hit@gmail.com.
  • Ming Yang
    Wuhan Institute for Food and Cosmetic Control, Wuhan 430014, China.
  • Xiaolong Wang
    Cardiovascular Department, Shuguang Hospital Affiliated to Shanghai University of TCM Shanghai, China.
  • Qingcai Chen
    Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China.
  • Buzhou Tang
  • Zhe Wang
    Department of Pathology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518033, China.
  • Hua Xu
    Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

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