A New Deep Learning Algorithm for Detecting the Lag Effect of Fine Particles on Hospital Emergency Visits for Respiratory Diseases

doi:10.1109/ACCESS.2020.3013543

	A New Deep Learning Algorithm for Detecting the Lag Effect of Fine Particles on Hospital Emergency Visits for Respiratory Diseases
	Lu, Jiaying 2,3,4; Bu, Pengju 5; Xia, Xiaolin 4,6; Yao, Ling 1,2,4; Zhang, Zhixin 7; Tan, Yuanju 7
刊名	IEEE ACCESS
	2020
卷号	8 页码:145593-145600
关键词	Fine particles (PM2.5) respiratory diseases prediction lag distribution effect distributed lag non-linear model (DLNM) long short-term memory (LSTM)
ISSN号	2169-3536
DOI	10.1109/ACCESS.2020.3013543
通讯作者	Yao, Ling(yaoling@lreis.ac.cn)
英文摘要	There exists a time lag between short-term exposure to fine particulate matter (PM2.5) and incidence of respiratory diseases. The quantification of length of the time lag is significant for preparation and allocation of relevant medical resources. Several classic lag analysis methods have been applied to determine this length. However, different models often lead to distinct results and which one is better is subtle. The prerequisite of obtaining the reliable length is that the model can truly reveal the underlying pattern hidden in the above relationship. Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the field of deep learning, whose strong capability makes it widely applied in many fields. In this study, we manage to exploit it to acquire the time-lag length in the exposure-response relationship. The relationship between exposure and response is assumed as linear and non-linear, and models with and without confounding factors are performed under these two assumptions. Results of DLNM model show that the best hospital emergency visit prediction appears in 3 lag days, with the maximum RR value of 1.004357 (95% CI: 1.000938-1.009563). Then, a vary of LSTM models with different time steps are performed, which are evaluated by mean absolute error (MAE), the mean absolute percentage error (MAPE), the root of mean square error (RMSE) and R square (R-2). The results show that LSTM of time step 3 achieves the lowest MAE (33), MAPE (9.86), RMSE (42) and the highest R-2 (0.78), consistent with the result of DLNM model. Also, the proposed model is compared with ARIMA model, one of the commonly used forecasting models, showing better accuracy. This demonstrates that LSTM can be used as a new method to detect the lag effect of PM2.5 on respiratory diseases.
资助项目	National Natural Science Foundation of China[41771380] ; Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory[GML2019ZD0301] ; GDAS' Project of Science and Technology Development[2020GDASYL-20200103003] ; National Data Sharing Infrastructure of Earth System Science
WOS关键词	PARTICULATE MATTER ; AIR-POLLUTION ; COARSE PARTICLES ; MORTALITY ; ASSOCIATIONS ; ADMISSIONS ; POPULATION ; MODELS
WOS研究方向	Computer Science ; Engineering ; Telecommunications
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000560350000001
资助机构	National Natural Science Foundation of China ; Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory ; GDAS' Project of Science and Technology Development ; National Data Sharing Infrastructure of Earth System Science
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/158004]
专题	中国科学院地理科学与资源研究所
通讯作者	Yao, Ling
作者单位	1.Nanjing Normal Univ, Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100101, Peoples R China 4.Southern Marine Sci & Engn Guangdong Lab, Guangzhou 511458, Peoples R China 5.Beijing Huayun Shinetek Sci & Technol Co Ltd, Beijing 100101, Peoples R China 6.Guangzhou Inst Geog, Guangdong Open Lab Geospatial Informat Technol &, Key Lab Guangdong Utilizat Remote Sensing & Geog, Engn Technol Ctr Remote Sensing Big Data Applicat, Guangzhou 510070, Peoples R China 7.China Japan Friendship Hosp, Int Med Serv, Beijing 100029, Peoples R China
推荐引用方式 GB/T 7714	Lu, Jiaying,Bu, Pengju,Xia, Xiaolin,et al. A New Deep Learning Algorithm for Detecting the Lag Effect of Fine Particles on Hospital Emergency Visits for Respiratory Diseases[J]. IEEE ACCESS,2020,8:145593-145600.
APA	Lu, Jiaying,Bu, Pengju,Xia, Xiaolin,Yao, Ling,Zhang, Zhixin,&Tan, Yuanju.(2020).A New Deep Learning Algorithm for Detecting the Lag Effect of Fine Particles on Hospital Emergency Visits for Respiratory Diseases.IEEE ACCESS,8,145593-145600.
MLA	Lu, Jiaying,et al."A New Deep Learning Algorithm for Detecting the Lag Effect of Fine Particles on Hospital Emergency Visits for Respiratory Diseases".IEEE ACCESS 8(2020):145593-145600.