Review of Relation Extraction in Electronic Medical Records

被引：0

作者：

Wang, Chen ^{[1
]}

Li, Ming ^{[1
]}

Ma, Jingang ^{[1
]}

机构：

[1] College of Intelligence and Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan

来源：

Computer Engineering and Applications | 2023年 / 59卷 / 16期

关键词：

deep learning; electronic medical records; pre-trained model; relation extraction;

D O I：

10.3778/j.issn.1002-8331.2209-0366

中图分类号：

学科分类号：

摘要：

The application of information extraction to electronic medical records has yielded rich research results, enabling the utilization of unstructured biomedical data. Relation extraction is an important subtask of information extraction and a bridge from data to knowledge. This paper provides a detailed classification of relation extraction based on different problems and different solutions of relation extraction. Relevant review tasks and representative datasets in the field of relation extraction for electronic medical records are collated. The progress of the application of relation extraction on electronic medical record texts is reviewed in stages, focusing on the wide application of deep learning methods on relation extraction and the progress of pre-trained models on the task of electronic medical record relation extraction at this stage. Finally, an outlook on the field is provided, highlighting the unresolved issues and future research directions. © 2023 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.

引用

页码：63 / 73

页数：10

共 70 条

[41]

MIWA M, BANSAL M., End-to-end relation extraction using LSTMs on sequences and tree structures[J], (2016)

[42]

HU Q, LIU N, WANG J，, Et al., An overlapping sequence tagging mechanism for symptoms and details extraction on Chinese medical records[J], Computers & Electrical Engineering, 91, (2021)

[43]

SONG L, ZHANG Y, GILDEA D, Et al., Leveraging dependency forest for neural medical relation extraction[J], (2019)

[44]

ZENG S, XU R, CHANG B, Et al., Double graph based reasoning for document-level relation extraction[J], (2020)

[45]

LI T, XIONG Y, WANG X, Et al., Document-level medical relation extraction via edge-oriented graph neural network based on document structure and external knowledge[J], BMC Medical Informatics and Decision Making, 21, 7, pp. 1-9, (2021)

[46]

SUN Q, XU T, ZHANG K, Et al., Dual-channel and hierarchical graph convolutional networks for document-level relation extraction[J], Expert Systems with Applications, 205, (2022)

[47]

FU T J, LI P H, MA W Y., GraphRel：modeling text as relational graphs for joint entity and relation extraction[C], Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 1409-1418, (2019)

[48]

TREISMAN A M, GELADE G., A feature-integration theory of attention[J], Cognitive Psychology, 12, 1, pp. 97-136, (1980)

[49]

ZHANG Z, ZHOU T, ZHANG Y，, Et al., Attention-based deep residual learning network for entity relation extraction in Chinese EMRs[J], BMC Medical Informatics and Decision Making, 19, 2, pp. 171-177, (2019)

[50]

VASWANI A, SHAZEER N, PARMAR N, Et al., Attention is all you need[C], Advances in Neural Information Processing Systems, 30, pp. 5998-6008, (2017)

← 1 2 3 4 5 6 7 →