Combining data augmentation and domain information with TENER model for Clinical Event Detection

Abstract Background In recent years, with the development of artificial intelligence, the use of deep learning technology for clinical information extraction has become a new trend. Clinical Event Detection (CED) as its subtask has attracted the attention from academia and industry. However, directl...

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Autores principales:	Zhichang Zhang, Dan Liu, Minyu Zhang, Xiaohui Qin
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Data augmentation Pre-trained language model Transformer Clinical Event Detection Electronic medical record Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/5c201c8ac7ca469c9e7989c205cf844d
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id	oai:doaj.org-article:5c201c8ac7ca469c9e7989c205cf844d
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spelling	oai:doaj.org-article:5c201c8ac7ca469c9e7989c205cf844d2021-11-21T12:28:52ZCombining data augmentation and domain information with TENER model for Clinical Event Detection10.1186/s12911-021-01618-31472-6947https://doaj.org/article/5c201c8ac7ca469c9e7989c205cf844d2021-11-01T00:00:00Zhttps://doi.org/10.1186/s12911-021-01618-3https://doaj.org/toc/1472-6947Abstract Background In recent years, with the development of artificial intelligence, the use of deep learning technology for clinical information extraction has become a new trend. Clinical Event Detection (CED) as its subtask has attracted the attention from academia and industry. However, directly applying the advancements in deep learning to CED task often yields unsatisfactory results. The main reasons are due to the following two points: (1) A great number of obscure professional terms in the electronic medical record leads to poor recognition performance of model. (2) The scarcity of datasets required for the task leads to poor model robustness. Therefore, it is urgent to solve these two problems to improve model performance. Methods This paper proposes a combining data augmentation and domain information with TENER Model for Clinical Event Detection. Results We use two evaluation metrics to compare the overall performance of the proposed model with the existing model on the 2012 i2b2 challenge dataset. Experimental results demonstrate that our proposed model achieves the best F1-score of 80.26%, type accuracy of 93% and Span F1-score of 90.33%, and outperforms the state-of-the-art approaches. Conclusions This paper proposes a multi-granularity information fusion encoder-decoder framework, which applies the TENER model to the CED task for the first time. It uses the pre-trained language model (BioBERT) to generate word-level features, solving the problem of a great number of obscure professional terms in the electronic medical record lead to poor recognition performance of model. In addition, this paper proposes a new data augmentation method for sequence labeling tasks, solving the problem of the scarcity of datasets required for the task leads to poor model robustness.Zhichang ZhangDan LiuMinyu ZhangXiaohui QinBMCarticleData augmentationPre-trained language modelTransformerClinical Event DetectionElectronic medical recordComputer applications to medicine. Medical informaticsR858-859.7ENBMC Medical Informatics and Decision Making, Vol 21, Iss S9, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Data augmentation Pre-trained language model Transformer Clinical Event Detection Electronic medical record Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Data augmentation Pre-trained language model Transformer Clinical Event Detection Electronic medical record Computer applications to medicine. Medical informatics R858-859.7 Zhichang Zhang Dan Liu Minyu Zhang Xiaohui Qin Combining data augmentation and domain information with TENER model for Clinical Event Detection
description	Abstract Background In recent years, with the development of artificial intelligence, the use of deep learning technology for clinical information extraction has become a new trend. Clinical Event Detection (CED) as its subtask has attracted the attention from academia and industry. However, directly applying the advancements in deep learning to CED task often yields unsatisfactory results. The main reasons are due to the following two points: (1) A great number of obscure professional terms in the electronic medical record leads to poor recognition performance of model. (2) The scarcity of datasets required for the task leads to poor model robustness. Therefore, it is urgent to solve these two problems to improve model performance. Methods This paper proposes a combining data augmentation and domain information with TENER Model for Clinical Event Detection. Results We use two evaluation metrics to compare the overall performance of the proposed model with the existing model on the 2012 i2b2 challenge dataset. Experimental results demonstrate that our proposed model achieves the best F1-score of 80.26%, type accuracy of 93% and Span F1-score of 90.33%, and outperforms the state-of-the-art approaches. Conclusions This paper proposes a multi-granularity information fusion encoder-decoder framework, which applies the TENER model to the CED task for the first time. It uses the pre-trained language model (BioBERT) to generate word-level features, solving the problem of a great number of obscure professional terms in the electronic medical record lead to poor recognition performance of model. In addition, this paper proposes a new data augmentation method for sequence labeling tasks, solving the problem of the scarcity of datasets required for the task leads to poor model robustness.
format	article
author	Zhichang Zhang Dan Liu Minyu Zhang Xiaohui Qin
author_facet	Zhichang Zhang Dan Liu Minyu Zhang Xiaohui Qin
author_sort	Zhichang Zhang
title	Combining data augmentation and domain information with TENER model for Clinical Event Detection
title_short	Combining data augmentation and domain information with TENER model for Clinical Event Detection
title_full	Combining data augmentation and domain information with TENER model for Clinical Event Detection
title_fullStr	Combining data augmentation and domain information with TENER model for Clinical Event Detection
title_full_unstemmed	Combining data augmentation and domain information with TENER model for Clinical Event Detection
title_sort	combining data augmentation and domain information with tener model for clinical event detection
publisher	BMC
publishDate	2021
url	https://doaj.org/article/5c201c8ac7ca469c9e7989c205cf844d
work_keys_str_mv	AT zhichangzhang combiningdataaugmentationanddomaininformationwithtenermodelforclinicaleventdetection AT danliu combiningdataaugmentationanddomaininformationwithtenermodelforclinicaleventdetection AT minyuzhang combiningdataaugmentationanddomaininformationwithtenermodelforclinicaleventdetection AT xiaohuiqin combiningdataaugmentationanddomaininformationwithtenermodelforclinicaleventdetection
_version_	1718419008423723008

Combining data augmentation and domain information with TENER model for Clinical Event Detection

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