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Early Warning Method for Public Health Emergency Under Artificial Neural Network in the Context of Deep Learning

The purpose is to minimize the substantial losses caused by public health emergencies to people’s health and daily life and the national economy. The tuberculosis data from June 2017 to 2019 in a city are collected. The Structural Equation Model (SEM) is constructed to determine the relationship bet...

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Detalles Bibliográficos
Autores principales: Zheng, Shuang, Hu, Xiaomei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7917260/
https://www.ncbi.nlm.nih.gov/pubmed/33658958
http://dx.doi.org/10.3389/fpsyg.2021.594031
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author Zheng, Shuang
Hu, Xiaomei
author_facet Zheng, Shuang
Hu, Xiaomei
author_sort Zheng, Shuang
collection PubMed
description The purpose is to minimize the substantial losses caused by public health emergencies to people’s health and daily life and the national economy. The tuberculosis data from June 2017 to 2019 in a city are collected. The Structural Equation Model (SEM) is constructed to determine the relationship between hidden and explicit variables by determining the relevant indicators and parameter estimation. The prediction model based on Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) is constructed. The method’s effectiveness is verified by comparing the prediction model’s loss value and accuracy in training and testing. Meanwhile, 50 pieces of actual cases are tested, and the warning level is determined according to the T-value. The results show that comparing and analyzing ANN, CNN, and the hybrid network of ANN and CNN, the hybrid network’s accuracy (95.1%) is higher than the other two algorithms, 89.1 and 90.1%. Also, the hybrid network has sound prediction effects and accuracy when predicting actual cases. Therefore, the early warning method based on ANN in deep learning has better performance in public health emergencies’ early warning, which is significant for improving early warning capabilities.
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spelling pubmed-79172602021-03-02 Early Warning Method for Public Health Emergency Under Artificial Neural Network in the Context of Deep Learning Zheng, Shuang Hu, Xiaomei Front Psychol Psychology The purpose is to minimize the substantial losses caused by public health emergencies to people’s health and daily life and the national economy. The tuberculosis data from June 2017 to 2019 in a city are collected. The Structural Equation Model (SEM) is constructed to determine the relationship between hidden and explicit variables by determining the relevant indicators and parameter estimation. The prediction model based on Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) is constructed. The method’s effectiveness is verified by comparing the prediction model’s loss value and accuracy in training and testing. Meanwhile, 50 pieces of actual cases are tested, and the warning level is determined according to the T-value. The results show that comparing and analyzing ANN, CNN, and the hybrid network of ANN and CNN, the hybrid network’s accuracy (95.1%) is higher than the other two algorithms, 89.1 and 90.1%. Also, the hybrid network has sound prediction effects and accuracy when predicting actual cases. Therefore, the early warning method based on ANN in deep learning has better performance in public health emergencies’ early warning, which is significant for improving early warning capabilities. Frontiers Media S.A. 2021-02-15 /pmc/articles/PMC7917260/ /pubmed/33658958 http://dx.doi.org/10.3389/fpsyg.2021.594031 Text en Copyright © 2021 Zheng and Hu. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychology
Zheng, Shuang
Hu, Xiaomei
Early Warning Method for Public Health Emergency Under Artificial Neural Network in the Context of Deep Learning
title Early Warning Method for Public Health Emergency Under Artificial Neural Network in the Context of Deep Learning
title_full Early Warning Method for Public Health Emergency Under Artificial Neural Network in the Context of Deep Learning
title_fullStr Early Warning Method for Public Health Emergency Under Artificial Neural Network in the Context of Deep Learning
title_full_unstemmed Early Warning Method for Public Health Emergency Under Artificial Neural Network in the Context of Deep Learning
title_short Early Warning Method for Public Health Emergency Under Artificial Neural Network in the Context of Deep Learning
title_sort early warning method for public health emergency under artificial neural network in the context of deep learning
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7917260/
https://www.ncbi.nlm.nih.gov/pubmed/33658958
http://dx.doi.org/10.3389/fpsyg.2021.594031
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