A Data Augmentation Method for War Trauma Using the War Trauma Severity Score and Deep Neural Networks

The demand for large-scale analysis and research of data on trauma from modern warfare is increasing day by day, but the amount of existing data is not sufficient to meet such demand. In this study, an integrated modeling approach incorporating a war trauma severity scoring algorithm (WTSS) and deep...

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Autores principales:	Jibin Yin, Pengfei Zhao, Yi Zhang, Yi Han, Shuoyu Wang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	artificial intelligence data augmentation war trauma severity score deep neural network Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/c9d666e6b1c24066acb989755ab85838
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spelling	oai:doaj.org-article:c9d666e6b1c24066acb989755ab858382021-11-11T15:39:26ZA Data Augmentation Method for War Trauma Using the War Trauma Severity Score and Deep Neural Networks10.3390/electronics102126572079-9292https://doaj.org/article/c9d666e6b1c24066acb989755ab858382021-10-01T00:00:00Zhttps://www.mdpi.com/2079-9292/10/21/2657https://doaj.org/toc/2079-9292The demand for large-scale analysis and research of data on trauma from modern warfare is increasing day by day, but the amount of existing data is not sufficient to meet such demand. In this study, an integrated modeling approach incorporating a war trauma severity scoring algorithm (WTSS) and deep neural networks (DNN) is proposed. First, the proposed WTSS, which uses multiple non-linear regression based on the characteristics of war trauma data and the medical evaluation by an expert panel, performed a standardized assessment of an injury and predicts its trauma consequences. Second, to generate virtual injury, based on the probability of occurrence, the injured parts, injury types, and complications were randomly sampled and combined, and then WTSS was used to assess the consequences of the virtual injury. Third, to evaluate the accuracy of the predicted injury consequences, we built a DNN classifier and then trained it with the generated data and tested it with real data. Finally, we used the Delphi method to filter out unreasonable injuries and improve data rationality. The experimental results verified that the proposed approach surpassed the traditional artificial generation methods, achieved a prediction accuracy of 84.43%, and realized large-scale and credible war trauma data augmentation.Jibin YinPengfei ZhaoYi ZhangYi HanShuoyu WangMDPI AGarticleartificial intelligencedata augmentationwar trauma severity scoredeep neural networkElectronicsTK7800-8360ENElectronics, Vol 10, Iss 2657, p 2657 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial intelligence data augmentation war trauma severity score deep neural network Electronics TK7800-8360
spellingShingle	artificial intelligence data augmentation war trauma severity score deep neural network Electronics TK7800-8360 Jibin Yin Pengfei Zhao Yi Zhang Yi Han Shuoyu Wang A Data Augmentation Method for War Trauma Using the War Trauma Severity Score and Deep Neural Networks
description	The demand for large-scale analysis and research of data on trauma from modern warfare is increasing day by day, but the amount of existing data is not sufficient to meet such demand. In this study, an integrated modeling approach incorporating a war trauma severity scoring algorithm (WTSS) and deep neural networks (DNN) is proposed. First, the proposed WTSS, which uses multiple non-linear regression based on the characteristics of war trauma data and the medical evaluation by an expert panel, performed a standardized assessment of an injury and predicts its trauma consequences. Second, to generate virtual injury, based on the probability of occurrence, the injured parts, injury types, and complications were randomly sampled and combined, and then WTSS was used to assess the consequences of the virtual injury. Third, to evaluate the accuracy of the predicted injury consequences, we built a DNN classifier and then trained it with the generated data and tested it with real data. Finally, we used the Delphi method to filter out unreasonable injuries and improve data rationality. The experimental results verified that the proposed approach surpassed the traditional artificial generation methods, achieved a prediction accuracy of 84.43%, and realized large-scale and credible war trauma data augmentation.
format	article
author	Jibin Yin Pengfei Zhao Yi Zhang Yi Han Shuoyu Wang
author_facet	Jibin Yin Pengfei Zhao Yi Zhang Yi Han Shuoyu Wang
author_sort	Jibin Yin
title	A Data Augmentation Method for War Trauma Using the War Trauma Severity Score and Deep Neural Networks
title_short	A Data Augmentation Method for War Trauma Using the War Trauma Severity Score and Deep Neural Networks
title_full	A Data Augmentation Method for War Trauma Using the War Trauma Severity Score and Deep Neural Networks
title_fullStr	A Data Augmentation Method for War Trauma Using the War Trauma Severity Score and Deep Neural Networks
title_full_unstemmed	A Data Augmentation Method for War Trauma Using the War Trauma Severity Score and Deep Neural Networks
title_sort	data augmentation method for war trauma using the war trauma severity score and deep neural networks
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/c9d666e6b1c24066acb989755ab85838
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A Data Augmentation Method for War Trauma Using the War Trauma Severity Score and Deep Neural Networks

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