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

• Published in: Nature communications Vol. 12; no. 1; p. 4575
• Main Authors: Ogata, Soshiro, Takegami, Misa, Ozaki, Taira, Nakashima, Takahiro, Onozuka, Daisuke, Murata, Shunsuke, Nakaoku, Yuriko, Suzuki, Koyu, Hagihara, Akihito, Noguchi, Teruo, Iihara, Koji, Kitazume, Keiichi, Morioka, Tohru, Yamazaki, Shin, Yoshida, Takahiro, Yamagata, Yoshiki, Nishimura, Kunihiro
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 28.07.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1305; 692/700/478; 704/106/694/2739/2807; Climate change; Generalized linear models; Heat stroke; Heatstroke; Humanities and Social Sciences; Learning algorithms; Machine learning; Meteorological data; multidisciplinary; Performance prediction; Population number; Prediction models; Public health; Science; Science (multidisciplinary); Spikes; Statistical models; Weather
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-021-24823-0

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. In the context of climate change, heatstroke is expected to become an increasingly relevant public health concern. Here, the authors develop and validate prediction models for the number of all heatstroke cases in different cities in Japan.