In Nature communications ; h5-index 260.0
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.
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