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

[1]

YANG J F, YU Q B，, GUAN Y，, Et al., An overview of research on electronic medical record oriented named entity recognition and entity relation extraction[J], Acta Automatica Sinica, 40, 8, pp. 1537-1562, (2014)

[2]

YANG J F，, GUAN Y，, HE B, Et al., Corpus construction for named entities and entity relations on Chinese electronic medical records[J], Journal of Software, 27, 11, pp. 2725-2746, (2016)

[3]

GRISHMAN R, SUNDHEIM B M., Message understanding conference-6：a brief history, Proceedings of the 16th International Conference on Computational Linguistics, (1996)

[4]

UZUNER O, SOUTH B R，, SHEN S, Et al., 2010 i2b2/VA challenge on concepts，assertions，and relations in clinical text[J], Journal of the American Medical Informatics Association, 18, 5, pp. 552-556, (2011)

[5]

ZHAN K, PENG W, XIONG Y，, Et al., Novel graph-based model with biaffine attention for family history extraction from clinical text：modeling study[J], JMIR Medical Informatics, 9, 4, (2021)

[6]

SEGURA-BEDMAR I, MARTINEZ P, HERRERO-ZAZO M., SemEval-2013 task 9：extraction of drug-drug interactions from biomedical texts（DDIExtraction 2013）[C], Proceedings of the 7th International Workshop on Semantic Evaluation, pp. 341-350, (2013)

[7]

LI J, SUN Y, JOHNSON R J, Et al., BioCreative V CDR ask corpus：a resource for chemical disease relation extracion[J], Database, (2016)

[8]

KRALLINGER M, LEITNER F, RODRIGUEZ-PENAGOS C，, Et al., Overview of the protein-protein interaction annotation extraction task of BioCreative II[J], Genome Biology, 9, 2, pp. 1-19, (2008)

[9]

CUI B, JIN T, WANG J M，, Et al., Overview of information extraction of free-text electronic medical records[J], Journal of Computer Applications, 9, 4, pp. 1055-1063, (2021)

[10]

NORIEGA-ATALA E, HEIN P D, THUMSI S S, Et al., Extracting inter-sentence relations for associating biological context with events in biomedical texts[J], IEEE/ACM Transactions on Computational Biology and Bioinformatics, 17, 6, pp. 1895-1906, (2019)

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