School of Management, Beijing University of Chinese Medicine, Beijing, China.
School of Humanities, Beijing University of Chinese Medicine, Beijing, China.
Front Public Health. 2024 Jul 22;12:1420608. doi: 10.3389/fpubh.2024.1420608. eCollection 2024.
Heatstroke is a serious clinical condition caused by exposure to high temperature and high humidity environment, which leads to a rapid increase of the core temperature of the body to more than 40°C, accompanied by skin burning, consciousness disorders and other organ system damage. This study aims to analyze the effect of meteorological factors on the incidence of heatstroke using machine learning, and to construct a heatstroke forecasting model to provide reference for heatstroke prevention.
The data of heatstroke incidence and meteorological factors in a city in South China from May to September 2014-2019 were analyzed in this study. The lagged effect of meteorological factors on heatstroke incidence was analyzed based on the distributed lag non-linear model, and the prediction model was constructed by using regression decision tree, random forest, gradient boosting trees, linear SVRs, LSTMs, and ARIMA algorithm.
The cumulative lagged effect found that heat index, dew-point temperature, daily maximum temperature and relative humidity had the greatest influence on heatstroke. When the heat index, dew-point temperature, and daily maximum temperature exceeded certain thresholds, the risk of heatstroke was significantly increased on the same day and within the following 5 days. The lagged effect of relative humidity on the occurrence of heatstroke was different with the change of relative humidity, and both excessively high and low environmental humidity levels exhibited a longer lagged effect on the occurrence of heatstroke. With regard to the prediction model, random forest model had the best performance of 5.28 on RMSE and dropped to 3.77 after being adjusted.
The incidence of heatstroke in this city is significantly correlated with heat index, heatwave, dew-point temperature, air temperature and , among which the heat index and dew-point temperature have a significant lagged effect on heatstroke incidence. Relevant departments need to closely monitor the data of the correlated factors, and adopt heat prevention measures before the temperature peaks, calling on citizens to reduce outdoor activities.
中暑是一种严重的临床病症，由暴露于高温高湿环境引起，导致人体核心温度迅速升高至 40°C 以上，伴有皮肤灼伤、意识障碍和其他器官系统损伤。本研究旨在利用机器学习分析气象因素对中暑发病率的影响，并构建中暑预测模型，为中暑预防提供参考。
本研究分析了华南某市 2014-2019 年 5 月至 9 月中暑发病率和气象因素数据。基于分布滞后非线性模型分析气象因素对中暑发病率的滞后效应，并利用回归决策树、随机森林、梯度提升树、线性 SVRs、LSTM 和 ARIMA 算法构建预测模型。
累积滞后效应发现，热指数、露点温度、日最高温度和相对湿度对中暑影响最大。当日及随后 5 日内，热指数、露点温度和日最高温度超过一定阈值时，中暑风险显著增加。相对湿度对中暑发生的滞后效应随相对湿度的变化而不同，过高和过低的环境湿度水平都对中暑发生有较长的滞后效应。就预测模型而言，随机森林模型的 RMSE 性能最佳，为 5.28，调整后降至 3.77。
该市的中暑发病率与热指数、热浪、露点温度、空气温度等显著相关，其中热指数和露点温度对中暑发病率有显著的滞后效应。相关部门需要密切监测相关因素的数据，并在温度达到峰值前采取防暑措施，呼吁市民减少户外活动。
