Medical Named Entity Extraction from Chinese Resident Admit Notes Using Character and Word Attention-Enhanced Neural Network

International Journal of Environmental Research and Public Health
Yan GaoLei Gu

Abstract

The resident admit notes (RANs) in electronic medical records (EMRs) is first-hand information to study the patient's condition. Medical entity extraction of RANs is an important task to get disease information for medical decision-making. For Chinese electronic medical records, each medical entity contains not only word information but also rich character information. Effective combination of words and characters is very important for medical entity extraction. We propose a medical entity recognition model based on a character and word attention-enhanced (CWAE) neural network for Chinese RANs. In our model, word embeddings and character-based embeddings are obtained through character-enhanced word embedding (CWE) model and Convolutional Neural Network (CNN) model. Then attention mechanism combines the character-based embeddings and word embeddings together, which significantly improves the expression ability of words. The new word embeddings obtained by the attention mechanism are taken as the input to bidirectional long short-term memory (BI-LSTM) and conditional random field (CRF) to extract entities. We extracted nine types of key medical entities from Chinese RANs and evaluated our model. The proposed method was compared w...Continue Reading

References

Oct 23, 1997·Neural Computation·S Hochreiter, J Schmidhuber
Jul 22, 2010·Journal of the Medical Library Association : JMLA·Nanette J Welton
Jan 6, 2017·IEEE Journal of Biomedical and Health Informatics·Daniele RaviGuang-Zhong Yang
Jul 11, 2018·IEEE Journal of Biomedical and Health Informatics·Benjamin ShickelParisa Rashidi
Sep 6, 2018·IEEE/ACM Transactions on Computational Biology and Bioinformatics·Lishuang Li, Yuxin Jiang

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