A New Deep Learning Algorithm for Detecting the Lag Effect of Fine Particles on Hospital Emergency Visits for Respiratory Diseases
文献类型：期刊论文
| 作者 | Lu, Jiaying2,3,4; Bu, Pengju5; Xia, Xiaolin4,6; Yao, Ling1,2,4; Zhang, Zhixin7; Tan, Yuanju7 |
| 刊名 | IEEE ACCESS
 |
| 出版日期 | 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. |
| WOS关键词 | PARTICULATE MATTER ; AIR-POLLUTION ; COARSE PARTICLES ; MORTALITY ; ASSOCIATIONS ; ADMISSIONS ; POPULATION ; MODELS |
| 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研究方向 | Computer Science ; Engineering ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:000560350000001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 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 |
| 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. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。