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Attention-based deep residual learning network for entity relation extraction in Chinese EMRs.

Abstract：

BACKGROUND:Electronic medical records (EMRs) contain a variety of valuable medical concepts and relations. The ability to recognize relations between medical concepts described in EMRs enables the automatic processing of clinical texts, resulting in an improved quality of health-related data analysis. Driven by the 2010 i2b2/VA Challenge Evaluation, the relation recognition problem in EMRs has been studied by many researchers to address this important aspect of EMR information extraction. METHODS:This paper proposes an Attention-Based Deep Residual Network (ResNet) model to recognize medical concept relations in Chinese EMRs. RESULTS:Our model achieves F1-score of 77.80% on the manually annotated Chinese EMRs corpus and outperforms the state-of-the-art approaches. CONCLUSION:The residual network-based model can reduce the negative impact of corpus noise to parameter learning, and the combination of character position attention mechanism will enhance the identification features of different type of entities.

journal_name

BMC Med Inform Decis Mak

journal_title

BMC medical informatics and decision making

authors

Zhang Z,Zhou T,Zhang Y,Pang Y

doi

10.1186/s12911-019-0769-0

subject

Has Abstract

pub_date

2019-04-09 00:00:00

pages

55

issue

Suppl 2

issn

1472-6947

pii

10.1186/s12911-019-0769-0

journal_volume

19

pub_type

杂志文章

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