Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network

Abstract Automatically extracting useful information from electronic medical records along with conducting disease diagnoses is a promising task for both clinical decision support(CDS) and neural language processing(NLP). Most of the existing systems are based on artificially constructed knowledge b...

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Autores principales:	Zhongliang Yang, Yongfeng Huang, Yiran Jiang, Yuxi Sun, Yu-Jin Zhang, Pengcheng Luo
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/cd5877d851224bbe80a6ade811daf43e
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spelling	oai:doaj.org-article:cd5877d851224bbe80a6ade811daf43e2021-12-02T15:07:47ZClinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network10.1038/s41598-018-24389-w2045-2322https://doaj.org/article/cd5877d851224bbe80a6ade811daf43e2018-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-24389-whttps://doaj.org/toc/2045-2322Abstract Automatically extracting useful information from electronic medical records along with conducting disease diagnoses is a promising task for both clinical decision support(CDS) and neural language processing(NLP). Most of the existing systems are based on artificially constructed knowledge bases, and then auxiliary diagnosis is done by rule matching. In this study, we present a clinical intelligent decision approach based on Convolutional Neural Networks(CNN), which can automatically extract high-level semantic information of electronic medical records and then perform automatic diagnosis without artificial construction of rules or knowledge bases. We use collected 18,590 copies of the real-world clinical electronic medical records to train and test the proposed model. Experimental results show that the proposed model can achieve 98.67% accuracy and 96.02% recall, which strongly supports that using convolutional neural network to automatically learn high-level semantic features of electronic medical records and then conduct assist diagnosis is feasible and effective.Zhongliang YangYongfeng HuangYiran JiangYuxi SunYu-Jin ZhangPengcheng LuoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-9 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Zhongliang Yang Yongfeng Huang Yiran Jiang Yuxi Sun Yu-Jin Zhang Pengcheng Luo Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network
description	Abstract Automatically extracting useful information from electronic medical records along with conducting disease diagnoses is a promising task for both clinical decision support(CDS) and neural language processing(NLP). Most of the existing systems are based on artificially constructed knowledge bases, and then auxiliary diagnosis is done by rule matching. In this study, we present a clinical intelligent decision approach based on Convolutional Neural Networks(CNN), which can automatically extract high-level semantic information of electronic medical records and then perform automatic diagnosis without artificial construction of rules or knowledge bases. We use collected 18,590 copies of the real-world clinical electronic medical records to train and test the proposed model. Experimental results show that the proposed model can achieve 98.67% accuracy and 96.02% recall, which strongly supports that using convolutional neural network to automatically learn high-level semantic features of electronic medical records and then conduct assist diagnosis is feasible and effective.
format	article
author	Zhongliang Yang Yongfeng Huang Yiran Jiang Yuxi Sun Yu-Jin Zhang Pengcheng Luo
author_facet	Zhongliang Yang Yongfeng Huang Yiran Jiang Yuxi Sun Yu-Jin Zhang Pengcheng Luo
author_sort	Zhongliang Yang
title	Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network
title_short	Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network
title_full	Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network
title_fullStr	Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network
title_full_unstemmed	Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network
title_sort	clinical assistant diagnosis for electronic medical record based on convolutional neural network
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/cd5877d851224bbe80a6ade811daf43e
work_keys_str_mv	AT zhongliangyang clinicalassistantdiagnosisforelectronicmedicalrecordbasedonconvolutionalneuralnetwork AT yongfenghuang clinicalassistantdiagnosisforelectronicmedicalrecordbasedonconvolutionalneuralnetwork AT yiranjiang clinicalassistantdiagnosisforelectronicmedicalrecordbasedonconvolutionalneuralnetwork AT yuxisun clinicalassistantdiagnosisforelectronicmedicalrecordbasedonconvolutionalneuralnetwork AT yujinzhang clinicalassistantdiagnosisforelectronicmedicalrecordbasedonconvolutionalneuralnetwork AT pengchengluo clinicalassistantdiagnosisforelectronicmedicalrecordbasedonconvolutionalneuralnetwork
_version_	1718388417610383360

Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network

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