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基于机器学习的气象因素和城市热中风疾病的分析和预测
Hui Xu1, Shufang Guo1, Xiaojun Shi1
1School of Management, Beijing University of Chinese Medicine, Beijing, China.
Frontiers in public health
|August 6, 2024
概括
高热量指数和露点温度显著增加热中风风险,其影响持续长达5天. 机器学习模型准确预测热中风的发生率,帮助预防工作.
科学领域:
- 环境健康 环境健康
- 公共卫生 公共卫生
- 机器学习应用 机器学习应用
背景情况:
- 热中风是一种与高温和湿度相关的严重疾病,导致危险的核心体温升高和器官损伤.
- 了解气象影响对于有效的热中风预防策略至关重要.
研究的目的:
- 用机器学习分析气象因素对热中风发生率的影响.
- 开发热中风的预测模型,以指导公共卫生干预.
主要方法:
- 分析中国南部热中风发生率和气象数据 (2014-2019).
- 应用分布式滞后非线性模型来评估天气变量的滞后影响.
- 使用诸如随机森林,LSTM和ARIMA之类的算法构建预测模型.
主要成果:
- 热量指数,露点温度,最高温度和相对湿度是影响热中风的关键因素.
- 超过热量指数,露点和最高温度的特定值,可显著增加热中风风险,持续长达5天.
- 随机森林模型展示了最好的预测性能.
结论:
- 热中风的发生率与热量指数,热浪,露点,空气温度和'zhongfu'有很强的相关性.
- 热量指数和露点温度对热中风具有显著的滞后影响.
- 监测气象因素和及时实施预防措施对于减轻热中风风险至关重要.
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