气候变化领域集成服务门户(CCPortal): Heatstroke predictions by machine learning, weather information, and an all-population registry for 12-hour heatstroke alerts

CCPortal

DOI	10.1038/s41467-021-24823-0
	Heatstroke predictions by machine learning, weather information, and an all-population registry for 12-hour heatstroke alerts
	Ogata S.; Takegami M.; Ozaki T.; Nakashima T.; Onozuka D.; Murata S.; Nakaoku Y.; Suzuki K.; Hagihara A.; Noguchi T.; Iihara K.; Kitazume K.; Morioka T.; Yamazaki S.; Yoshida T.; Yamagata Y.; Nishimura K.
发表日期	2021
ISSN	2041-1723
卷号	12 期号:1
英文摘要	This study aims to develop and validate prediction models for the number of all heatstroke cases, and heatstrokes of hospital admission and death cases per city per 12 h, using multiple weather information and a population-based database for heatstroke patients in 16 Japanese cities (corresponding to around a 10,000,000 population size). In the testing dataset, mean absolute percentage error of generalized linear models with wet bulb globe temperature as the only predictor and the optimal models, respectively, are 43.0% and 14.8% for spikes in the number of all heatstroke cases, and 37.7% and 10.6% for spikes in the number of heatstrokes of hospital admission and death cases. The optimal models predict the spikes in the number of heatstrokes well by machine learning methods including non-linear multivariable predictors and/or under-sampling and bagging. Here, we develop prediction models whose predictive performances are high enough to be implemented in public health settings. © 2021, The Author(s).
语种	英语
scopus关键词	cardiovascular disease; database; machine learning; multivariate analysis; performance assessment; prediction; public health; sampling; Elliptio dilatata; heat stroke; human; information processing; machine learning; mortality; register; temperature; weather; Data Management; Heat Stroke; Humans; Machine Learning; Registries; Temperature; Weather
来源期刊	Nature Communications
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/250646
作者单位	Department of Preventive Medicine and Epidemiology, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan; Department of Civil, Environmental and Applied Systems Engineering, Faculty of Environmental and Urban Engineering, Kansai University, Suita, Osaka, Japan; Department of Cardiovascular Medicine, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan; Director General, National Cerebral and Cardiovascular Center Hospital, Suita, Osaka, Japan; Health and Environmental Risk Division, National Institute for Environmental Studies, Tsukuba, Ibaraki, Japan; Earth System Division, National Institute for Environmental Studies, Tsukuba, Ibaraki, Japan; Department of Urban Engineering, School of Engineering, The University of Tokyo, Tokyo, Japan; Graduate School of System Design and Management, Keio University, Yokohama, Kanagawa, Japan
推荐引用方式 GB/T 7714	Ogata S.,Takegami M.,Ozaki T.,et al. Heatstroke predictions by machine learning, weather information, and an all-population registry for 12-hour heatstroke alerts[J],2021,12(1).
APA	Ogata S..,Takegami M..,Ozaki T..,Nakashima T..,Onozuka D..,...&Nishimura K..(2021).Heatstroke predictions by machine learning, weather information, and an all-population registry for 12-hour heatstroke alerts.Nature Communications,12(1).
MLA	Ogata S.,et al."Heatstroke predictions by machine learning, weather information, and an all-population registry for 12-hour heatstroke alerts".Nature Communications 12.1(2021).