Application of a long short-term memory neural network: a burgeoning method of deep learning in forecasting HIV incidence in Guangxi, China
Application of a long short-term memory neural network: a burgeoning method of deep learning in forecasting HIV incidence in Guangxi, China
复制标题
长短期记忆神经网络的应用：一种新兴的深度学习方法在中国广西的艾滋病毒发病率预测中的应用
DOI：
10.1017/s095026881900075x
复制
发表时间：
2019-01-01
影响因子：
4.2
通讯作者：
Ye, L.
中科院分区：
文献类型：
--
作者：
Wang, G.;Wei, W.;Ye, L.
广西是中国西南部的一个省，艾滋病报告病例数居全国第二位。本研究旨在建立一个准确、有效的模型来描述广西艾滋病的流行趋势，并预测其发病率。广西2005 - 2016年艾滋病发病率数据来自中国疾病预防控制中心数据库。使用长短期记忆（LSTM）神经网络模型、自回归积分移动平均（ARIMA）模型、广义回归神经网络（GRNN）模型和指数平滑（ES）来拟合发病率数据。2015年和2016年的数据被用来验证最合适的模型。模型的性能进行了评估指标，包括均方误差（MSE），均方根误差，平均绝对误差和平均绝对百分比误差。当N值（时间步长）为12时，LSTM模型的MSE最低。2015年和2016年发病率最合适的ARIMA模型分别为ARIMA（1，1，2）（0，1，2）12和ARIMA（2，1，0）（1，1，2）12。GRNN和ES模型对广西HIV发病率的预测精度较差。LSTM模型的四个性能指标都低于ARIMA，GRNN和ES模型。LSTM模型比其他时间序列模型更有效，对于监测和控制当地艾滋病疫情非常重要。
Abstract Guangxi, a province in southwestern China, has the second highest reported number of HIV/AIDS cases in China. This study aimed to develop an accurate and effective model to describe the tendency of HIV and to predict its incidence in Guangxi. HIV incidence data of Guangxi from 2005 to 2016 were obtained from the database of the Chinese Center for Disease Control and Prevention. Long short-term memory (LSTM) neural network models, autoregressive integrated moving average (ARIMA) models, generalised regression neural network (GRNN) models and exponential smoothing (ES) were used to fit the incidence data. Data from 2015 and 2016 were used to validate the most suitable models. The model performances were evaluated by evaluating metrics, including mean square error (MSE), root mean square error, mean absolute error and mean absolute percentage error. The LSTM model had the lowest MSE when the N value (time step) was 12. The most appropriate ARIMA models for incidence in 2015 and 2016 were ARIMA (1, 1, 2) (0, 1, 2)12 and ARIMA (2, 1, 0) (1, 1, 2)12, respectively. The accuracy of GRNN and ES models in forecasting HIV incidence in Guangxi was relatively poor. Four performance metrics of the LSTM model were all lower than the ARIMA, GRNN and ES models. The LSTM model was more effective than other time-series models and is important for the monitoring and control of local HIV epidemics.