Zheng Shuang, Hu Xiaomei
College of Media and International Culture, Zhejiang University, Hangzhou, China.
School of Media and Law, NingboTech University, Ningbo, China.
Front Psychol. 2021 Feb 15;12:594031. doi: 10.3389/fpsyg.2021.594031. eCollection 2021.
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 -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.
目的是尽量减少突发公共卫生事件对人民健康、日常生活和国民经济造成的重大损失。收集了某城市2017年6月至2019年的结核病数据。构建结构方程模型（SEM），通过确定相关指标和参数估计来确定隐变量和显变量之间的关系。构建基于人工神经网络（ANN）和卷积神经网络（CNN）的预测模型。通过比较预测模型在训练和测试中的损失值和准确率来验证该方法的有效性。同时，对50个实际案例进行测试，并根据P值确定预警级别。结果表明，对比分析ANN、CNN以及ANN与CNN的混合网络，混合网络的准确率（95.1%）高于其他两种算法，分别为89.1%和90.1%。此外，混合网络在预测实际案例时具有良好的预测效果和准确率。因此，深度学习中基于ANN的预警方法在突发公共卫生事件预警方面具有更好的性能，这对于提高预警能力具有重要意义。
