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Feasibility of machine learning methods for predicting hospital emergency room visits for respiratory diseases

文献类型：期刊论文

作者Lu, Jiaying4,5,6; Bu, Pengju3; Xia, Xiaolin2,4; Lu, Ning1,4,6; Yao, Ling1,4,6; Jiang, Hou6
刊名ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
出版日期2021-02-10
页码9
关键词PM2 5 exposure Respiratory diseases Emergency room visits Machine learning
ISSN号0944-1344
DOI10.1007/s11356-021-12658-7
通讯作者Yao, Ling(yaoling@lreis.ac.cn)
英文摘要The prediction of hospital emergency room visits (ERV) for respiratory diseases after the outbreak of PM2.5 is of great importance in terms of public health, medical resource allocation, and policy decision support. Recently, the machine learning methods bring promising solutions for ERV prediction in view of their powerful ability of short-term forecasting, while their performances still exist unknown. Therefore, we aim to check the feasibility of machine learning methods for ERV prediction of respiratory diseases. Three different machine learning models, including autoregressive integrated moving average (ARIMA), multilayer perceptron (MLP), and long short-term memory (LSTM), are introduced to predict daily ERV in urban areas of Beijing, and their performances are evaluated in terms of the mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R-2). The results show that the performance of ARIMA is the worst, with a maximum R-2 of 0.70 and minimum MAE, RMSE, and MAPE of 99, 124, and 26.56, respectively, while MLP and LSTM perform better, with a maximum R-2 of 0.80 (0.78) and corresponding MAE, RMSE, and MAPE of 49 (33), 62 (42), and 14.14 (9.86). In addition, it demonstrates that MLP cannot detect the time lag effect properly, while LSTM does well in the description and prediction of exposure-response relationship between PM2.5 pollution and infecting respiratory disease.
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] ; China Postdoctoral Science Foundation[2020M682628] ; State Key Laboratory of Resources and Environmental Information System
WOS研究方向Environmental Sciences & Ecology
语种英语
WOS记录号WOS:000616913700010
出版者SPRINGER HEIDELBERG
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 ; China Postdoctoral Science Foundation ; State Key Laboratory of Resources and Environmental Information System
源URL[http://ir.igsnrr.ac.cn/handle/311030/160623]  
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.Guangzhou Inst Geog, Engn Technol Ctr Remote Sensing Big Data Applicat, Guangdong Open Lab Geospatial Informat Technol &, Key Lab Guangdong Utilizat Remote Sensing & Geog, Guangzhou 510070, Peoples R China
3.Beijing Huayun Shinetek Sci & Technol Co Ltd, Beijing 100101, Peoples R China
4.Southern Marine Sci & Engn Guangdong Lab, Guangzhou 511458, Peoples R China
5.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100101, Peoples R China
6.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Lu, Jiaying,Bu, Pengju,Xia, Xiaolin,et al. Feasibility of machine learning methods for predicting hospital emergency room visits for respiratory diseases[J]. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH,2021:9.
APA Lu, Jiaying,Bu, Pengju,Xia, Xiaolin,Lu, Ning,Yao, Ling,&Jiang, Hou.(2021).Feasibility of machine learning methods for predicting hospital emergency room visits for respiratory diseases.ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH,9.
MLA Lu, Jiaying,et al."Feasibility of machine learning methods for predicting hospital emergency room visits for respiratory diseases".ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH (2021):9.

入库方式： OAI收割

来源：地理科学与资源研究所

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