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J Biomed Inform. 2019 Apr;92:103133. doi: 10.1016/j.jbi.2019.103133. Epub 2019 Feb 25.

Incorporating dictionaries into deep neural networks for the Chinese clinical named entity recognition.

Author information

1
School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China.
2
School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China. Electronic address: ymzhou@ecust.edu.cn.
3
School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China. Electronic address: ruantong@ecust.edu.cn.
4
School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China. Electronic address: gaodaqi@ecust.edu.cn.
5
Shanghai Hospital Development Center, Shanghai 200040, China. Electronic address: heping@shdc.org.cn.

Abstract

Clinical named entity recognition aims to identify and classify clinical terms such as diseases, symptoms, treatments, exams, and body parts in electronic health records, which is a fundamental and crucial task for clinical and translational research. In recent years, deep neural networks have achieved significant success in named entity recognition and many other natural language processing tasks. Most of these algorithms are trained end to end, and can automatically learn features from large scale labeled datasets. However, these data-driven methods typically lack the capability of processing rare or unseen entities. Previous statistical methods and feature engineering practice have demonstrated that human knowledge can provide valuable information for handling rare and unseen cases. In this paper, we propose a new model which combines data-driven deep learning approaches and knowledge-driven dictionary approaches. Specifically, we incorporate dictionaries into deep neural networks. In addition, two different architectures that extend the bi-directional long short-term memory neural network and five different feature representation schemes are also proposed to handle the task. Computational results on the CCKS-2017 Task 2 benchmark dataset show that the proposed method achieves the highly competitive performance compared with the state-of-the-art deep learning methods.

KEYWORDS:

Clinical named entity recognition; Deep neural network; Dictionary features; Electronic health records

PMID:
30818005
DOI:
10.1016/j.jbi.2019.103133

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