Influenza-like illness prediction using a long short-term memory deep learning model with multiple open data sources

doi:10.1007/s11227-020-03182-5

中山醫學大學機構典藏 CSMUIR > 中山醫學大學研究成果 > 期刊論文 > Item 310902500/24612

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题名:	Influenza-like illness prediction using a long short-term memory deep learning model with multiple open data sources
作者:	Yang, CT;Chen, YA;Chan, YW;Lee, CL;Tsan, YT;Chan, WC;Liu, PY
关键词:	Influenza-like illness;LSTM;PM2;5;Deep learning
日期:	2020
上传时间:	2022-08-09T08:05:11Z (UTC)
出版者:	SPRINGER
ISSN:	0920-8542
摘要:	The influenza problem has always been an important global issue. It not only affects people's health problems but is also an essential topic of governments and health care facilities. Early prediction and response is the most effective control method for flu epidemics. It can effectively predict the influenza-like illness morbidity, and provide reliable information to the relevant facilities. For social facilities, it is possible to strengthen epidemic prevention and care for highly sick groups. It can also be used as a reminder for the public. This study collects information on the influenza-like illness emergency department visits to the Taiwan Centers for Disease Control, and the PM2.5 open-source data from the Taiwan Environmental Protection Administration's air quality monitoring network. By using deep learning techniques, the relevance of short-term estimates and the outbreak calculation method can be determined. The techniques are published by the WHO to determine whether the influenza-like illness situation is still in a stage of reasonable control. Finally, historical data and future forecasted data are integrated on the web page for visual presentation, to show the actual regional air quality situation and influenza-like illness data and to predict whether there is an outbreak of influenza in the region.
URI:	http://dx.doi.org/10.1007/s11227-020-03182-5 https://www.webofscience.com/wos/woscc/full-record/WOS:000516343900001 https://ir.csmu.edu.tw:8080/handle/310902500/24612
關聯:	JOURNAL OF SUPERCOMPUTING ,2020 ,v76 ,issue 12 ,p9303-9329
显示于类别:	[中山醫學大學研究成果] 期刊論文

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