TY - GEN
T1 - A multiclass classification method based on deep learning for named entity recognition in electronic medical records
AU - Dong, Xishuang
AU - Qian, Lijun
AU - Guan, Yi
AU - Huang, Lei
AU - Yu, Qiubin
AU - Yang, Jinfeng
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/11/17
Y1 - 2016/11/17
N2 - Research of named entity recognition (NER) on electrical medical records (EMRs) focuses on verifying whether methods to NER in traditional texts are effective for that in EMRs, and there is no model proposed for enhancing performance of NER via deep learning from the perspective of multiclass classification. In this paper, we annotate a real EMR corpus to accomplish the model training and evaluation. And, then, we present a Convolutional Neural Network (CNN) based multiclass classification method for mining named entities from EMRs. The method consists of two phases. In the phase 1, EMRs are pre-processed for representing samples with word embedding. In the phase 2, the method is built by segmenting training data into many subsets and training a CNN binary classification model on each of subset. Experimental results showed the effectiveness of our method.
AB - Research of named entity recognition (NER) on electrical medical records (EMRs) focuses on verifying whether methods to NER in traditional texts are effective for that in EMRs, and there is no model proposed for enhancing performance of NER via deep learning from the perspective of multiclass classification. In this paper, we annotate a real EMR corpus to accomplish the model training and evaluation. And, then, we present a Convolutional Neural Network (CNN) based multiclass classification method for mining named entities from EMRs. The method consists of two phases. In the phase 1, EMRs are pre-processed for representing samples with word embedding. In the phase 2, the method is built by segmenting training data into many subsets and training a CNN binary classification model on each of subset. Experimental results showed the effectiveness of our method.
KW - convolutional neural network
KW - electrical medical records
KW - machine learning
KW - named entity recognition
KW - natural language processing
UR - https://www.scopus.com/pages/publications/85006900495
U2 - 10.1109/NYSDS.2016.7747810
DO - 10.1109/NYSDS.2016.7747810
M3 - 会议稿件
AN - SCOPUS:85006900495
T3 - 2016 New York Scientific Data Summit, NYSDS 2016 - Proceedings
BT - 2016 New York Scientific Data Summit, NYSDS 2016 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 New York Scientific Data Summit, NYSDS 2016
Y2 - 14 August 2016 through 17 August 2016
ER -