Heatstroke predictions by machine learning, weather information, and an all-population registry for 12-hour heatstroke alerts

Soshiro Ogata, Misa Takegami, Taira Ozaki, Takahiro Nakashima, Daisuke Onozuka, Shunsuke Murata, Yuriko Nakaoku, Koyu Suzuki, Akihito Hagihara, Teruo Noguchi, Koji Iihara, Keiichi Kitazume, Tohru Morioka, Shin Yamazaki, Takahiro Yoshida, Yoshiki Yamagata and Kunihiro Nishimura ()
Additional contact information
Soshiro Ogata: National Cerebral and Cardiovascular Center
Misa Takegami: National Cerebral and Cardiovascular Center
Taira Ozaki: Kansai University
Takahiro Nakashima: National Cerebral and Cardiovascular Center
Daisuke Onozuka: National Cerebral and Cardiovascular Center
Shunsuke Murata: National Cerebral and Cardiovascular Center
Yuriko Nakaoku: National Cerebral and Cardiovascular Center
Koyu Suzuki: National Cerebral and Cardiovascular Center
Akihito Hagihara: National Cerebral and Cardiovascular Center
Teruo Noguchi: National Cerebral and Cardiovascular Center
Koji Iihara: Director General, National Cerebral and Cardiovascular Center Hospital
Keiichi Kitazume: Kansai University
Tohru Morioka: Kansai University
Shin Yamazaki: National Institute for Environmental Studies
Takahiro Yoshida: National Institute for Environmental Studies
Yoshiki Yamagata: National Institute for Environmental Studies
Kunihiro Nishimura: National Cerebral and Cardiovascular Center

Nature Communications, 2021, vol. 12, issue 1, 1-11

Abstract: Abstract 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.

Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.nature.com/articles/s41467-021-24823-0 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-24823-0

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-021-24823-0

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().