Heatstroke predictions by machine learning, weather information, and an all-population registry for 12-hour heatstroke alerts.
Nat Commun
; 12(1): 4575, 2021 07 28.
Article
in En
| MEDLINE
| ID: mdl-34321480
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Weather
/
Heat Stroke
/
Machine Learning
Type of study:
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
Language:
En
Journal:
Nat Commun
Journal subject:
BIOLOGIA
/
CIENCIA
Year:
2021
Document type:
Article
Affiliation country:
Japan