气候污染相互作用对心肺病患者住院治疗的短期滞后影响:一项使用AE+LSTM混合模型在日本进行的多城市预测研究
Yi Jia Chen1, Fan Zhao1, Qing Yang Wu2
1Department of Environment Systems, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba 277-8563, Japan.
Biomedical and environmental sciences : BES
|December 10, 2025
概括
短期气候和空气污染对心血管和呼吸系统疾病的住院患者产生影响. 深度学习模型有效地预测每天的入院,帮助公共卫生战略.
科学领域:
- 环境健康 环境健康
- 流行病学 流行病学
- 人工智能的人工智能
背景情况:
- 气候变化和空气污染对公共卫生构成重大挑战.
- 了解环境因素对健康的短期影响对于及时干预至关重要.
研究的目的:
- 评估气候和空气污染对心血管疾病 (CVD) 和呼吸道疾病 (RDs) 住院的滞后影响.
- 开发和评估深度学习模型,用于预测每日入院的病例.
主要方法:
- 在日本的多个城市 (东京,大阪,名古屋) 进行的一项研究.
- 随机森林模型分析环境因素对住院的滞后效应 (lag0,lag3,lag7).
- 混合自编码器-长短期记忆 (AE+LSTM) 深度学习模型用于预测.
主要成果:
- 空气污染物,特别是氧化 (NOx),对CVD和RD吸收产生了立即强烈的影响 (lag0).
- 温度和降水在较长的滞后期成为更强的预测因素 (滞后3,滞后7).
- 与标准LSTM相比,AE+LSTM模型显著提高了RD (32.4%) 和CVD (20.94%) 的预测准确度.
结论:
- 环境暴露带来了动态的,时间变化的健康风险.
- 深度学习模型为预测短期住院预测提供了强大的工具.
- 这种预测框架可以提高公共卫生准备和资源分配.
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