Entity Recognition of Chinese Electronic Medical Record Based on Gated Graph Neural Network

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外文摘要：Named entity recognition for Chinese electronic medical records aims to identify and classify clinical terms。 At present， most methods based on deep learning have weak points， such as insufficient feature extraction， ineffective identification of rare entities， and inaccurate boundary division。 A novel approach to solve these problems is proposed in this paper。 The approach uses graph neural networks with a multidigraph structure to capture the features that the medical dictionary offers; a convolutional neural network (CNN) is employed to obtain the boundary features between words。 These two features are concatenated into one feature vector and input to the Bi-Mogrifier LSTM-Attention module。 The proposed model integrates dictionary features into the deep neural network to better deal with rare clinical named entities。 The Multi-Head Attention module emphasizes the correlation and dependence between current information and context information in many aspects。 It weakens the weight of semantic features irrelevant to medical and clinical treatment。 The experimental results show that the proposed model is superior to the mainstream sequence labeling model， and the FI value of 95。31% is achieved。

外文关键词：

Deep learningTrainingDictionariesMechatronicsSplicingSemanticsGraph neural networks

作者：

Jin Zong、Jungang Han

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作者单位：

School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an, China

会议名称：

International Conference on Measuring Technology and Mechatronics Automation

会议地点：

Changsha(CN)

会议母体文献：

2022 14th International Conference on Measuring Technology and Mechatronics Automation

页码：

1208-1213

出版时间：

2022

DOI：

10.1109/ICMTMA54903.2022.00242