Named Entity Recognition of Chinese Electronic Medical Records Based on Multi-Feature Fusion

被引：0

作者：

Sun, Zhen ^{[1
]}

Li, Xinfu ^{[1
]}

机构：

[1] College of Cyberspace Security and Computer, Hebei University, Hebei, Baoding

来源：

Computer Engineering and Applications | 2023年 / 59卷 / 23期

关键词：

electronic medical records; gated recurrent unit (GRU); multi-feature; named entity recognition;

D O I：

10.3778/j.issn.1002-8331.2207-0455

中图分类号：

学科分类号：

摘要：

Named entity recognition is a basic task in natural language processing. Currently, the research on named entity recognition in Chinese electronic medical records does not consider the complex structure of medical texts and the uneven distribution of entity types in data sets. It only migrates the named entity recognition model from the general field to the medical field, and the recognition effect is not good. Aiming at the above problems, this paper proposes a multi-feature fusion named entity recognition model for Chinese electronic medical records. Firstly, the characters, radicals, and quadrilateral vectors are obtained to enrich the semantic representation of medical texts through Chinese characters. Secondly, the entity label labeling strategy is used to label the entity types in the vector to enhance the model’s learning of different text data types. Finally, the fusion vector is fed into the Mogrifier GRU layer to strengthen the relationship between feature representation semantics further, and CRF is used to establish label constraints. The experimental results show that the F1 value of the proposed model reaches 88.72% on the CCKS2019 dataset and 95.44% on the MSRA dataset, which verifies the effectiveness of the model. © 2023 Chinese Medical Journals Publishing House Co.Ltd. All rights reserved.

引用

页码：136 / 144

页数：8

共 28 条

[1]

ZHAO D D, HUANG D G, MENG J N, Et al., Chinese named entity recognition by intergrating multi- heads attention mechanism and character and words fusion, Computer Engineering and Applications, 58, 7, pp. 142-149, (2022)

[2]

KRAUS S, BLAKE C, WEST S L., Information extraction from medical notes, Opening Schools for All, 13, pp. 95-103, (2007)

[3]

E x traction of semantic biomedical relations from text using conditional random fields[J], BMC Bioinformatics, 9, 1, pp. 1-14, (2008)

[4]

YANG X, HUANG W., A conditional random fields approach to clinical name entity recognition[C], Proceedings of CCKS Tasks, pp. 1-6, (2018)

[5]

COLLOBERT R, WESTON J, BOTTOU L, Et al., Natural language processing(almost) from scratch[J], Journal of Machine Learning Research, 12, pp. 2493-2537, (2011)

[6]

HUANG Z, XU W, YU K., Bidirectional LSTM- CRF models for sequence tagging, (2015)

[7]

TANG G Q, GAO D Q, RUAN T, Et al., Named entity recognition of clinical electronic medical records with language model and attention mechanism, Journal of Computer Science, 47, 3, pp. 211-216, (2020)

[8]

TANG B, WANG X, YAN J., Entity recognition in Chinese clinical text using attention-based CNN-LSTM-CRF[J], BMC Medical Informatics and Decision Making, 19, pp. 74-82, (2019)

[9]

JI B, LIU R, LI S S, Et al., A BiLSTM-CRF method to Chinese electronic medical record named entity recognition[C], Proceedings of the 2018 International Conference on Algorithms,Computing and Artificial Intelligence, pp. 1-6, (2018)

[10]

LI B, KANG X D, ZHANG H L, Et al., Named entity recognition in Chinese electronic medical records using transformer- CRF, Computer Engineering and Applications, 56, 5, pp. 153-159, (2020)

← 1 2 3 →