TY - GEN
T1 - Transfer bi-directional LSTM RNN for named entity recognition in Chinese electronic medical records
AU - Dong, Xishuang
AU - Chowdhury, Shanta
AU - Qian, Lijun
AU - Guan, Yi
AU - Yang, Jinfeng
AU - Yu, Qiubin
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/12/14
Y1 - 2017/12/14
N2 - In this paper, a transfer bi-directional recurrent neural networks (RNN) is proposed for named entity recognition (NER) in Chinese electronic medical records (EMRs) that aims to extract medical knowledge such as phrases recording diseases and treatments automatically. We propose a two-step procedure where the first step is to train a shallow bi-directional RNN in the general domain, and the second step is to transfer knowledge from the general domain to train a deeper bi-directional RNN for recognizing medical concepts from Chinese EMRs. Specifically, this is achieved by initializing the shallow parts of the deeper network in the second step with parameter weights from the bi-directional RNN trained in the first step. Then the deeper networks are re-trained on the Chinese EMRs. Experimental results show that NER performances are improved by the transferred knowledge significantly.
AB - In this paper, a transfer bi-directional recurrent neural networks (RNN) is proposed for named entity recognition (NER) in Chinese electronic medical records (EMRs) that aims to extract medical knowledge such as phrases recording diseases and treatments automatically. We propose a two-step procedure where the first step is to train a shallow bi-directional RNN in the general domain, and the second step is to transfer knowledge from the general domain to train a deeper bi-directional RNN for recognizing medical concepts from Chinese EMRs. Specifically, this is achieved by initializing the shallow parts of the deeper network in the second step with parameter weights from the bi-directional RNN trained in the first step. Then the deeper networks are re-trained on the Chinese EMRs. Experimental results show that NER performances are improved by the transferred knowledge significantly.
KW - Electronic Medical Records
KW - Named Entity Recognition
KW - Recurrent Neural Networks
KW - Transfer Learning
UR - https://www.scopus.com/pages/publications/85048539265
U2 - 10.1109/HealthCom.2017.8210840
DO - 10.1109/HealthCom.2017.8210840
M3 - 会议稿件
AN - SCOPUS:85048539265
T3 - 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services, Healthcom 2017
SP - 1
EP - 4
BT - 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services, Healthcom 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th IEEE International Conference on e-Health Networking, Applications and Services, Healthcom 2017
Y2 - 12 October 2017 through 15 October 2017
ER -