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

نویسندگان

چکیده

The demand for large-scale analysis and research of data on trauma from modern warfare is increasing day by day, but the amount existing not sufficient to meet such demand. In this study, an integrated modeling approach incorporating a war severity scoring algorithm (WTSS) deep neural networks (DNN) proposed. First, proposed WTSS, which uses multiple non-linear regression based characteristics medical evaluation expert panel, performed standardized assessment injury predicts its consequences. Second, generate virtual injury, probability occurrence, injured parts, types, complications were randomly sampled combined, then WTSS was used assess consequences injury. Third, evaluate accuracy predicted consequences, we built DNN classifier trained it with generated tested real data. Finally, Delphi method filter out unreasonable injuries improve rationality. experimental results verified that surpassed traditional artificial generation methods, achieved prediction 84.43%, realized credible augmentation.

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ژورنال

عنوان ژورنال: Electronics

سال: 2021

ISSN: ['2079-9292']

DOI: https://doi.org/10.3390/electronics10212657