使用基于深度学习算法的环境因素预测喘患者数量
Hyemin Hwang1, Jae-Hyuk Jang2, Eunyoung Lee3
1Environmental Engineering Department, Ajou University, Suwon, 16499, Korea.
喘恶化受到流感,温度,PM10和NO2等空气污染物以及花粉的影响. 长期短期记忆 (LSTM) 模型有效地捕捉了这些影响喘患者数量的复杂环境因素.
科学领域:
- 环境健康 环境健康
- 流行病学 流行病学
- 计算生物学 计算生物学
背景情况:
- 喘恶化通常是由空气污染,天气,花粉和流感引发的.
- 现有的研究经常使用更简单的模型,无法捕捉这些因素之间的复杂相互作用.
- 深度学习为模拟这些复杂的关系提供了潜力,但需要进一步调查.
研究的目的:
- 用深度学习模型预测喘急诊室和门诊诊所的访问.
- 识别和量化环境因素对喘恶化的影响.
- 探索复发性神经网络 (RNN) 变体对于流行病学建模的有用性.
主要方法:
- 利用了2015-2019年关于空气污染物,天气,花粉和流感的数据.
- 采用长期短期记忆 (LSTM),门式循环单元 (GRU) 和基本的RNN模型进行预测.
- 使用特征重要性分析量化环境因素的相对重要性.
主要成果:
- 在模拟喘患者数量方面,LSTM表现出卓越的表现.
- 流感,温度,PM10,NO2,CO和花粉被确定为导致喘恶化的重要因素.
- 使用每周数据和假日计数有效建模季节性模式和假日效应.
结论:
- LSTM擅长模拟复杂的,非线性流行病学关系与滞后的反应和相互作用.
- 调查结果为决策者提供了关于喘恶化环境触发因素的宝贵见解.
- 这项研究强调了深度学习在理解和管理喘流行病学的潜力.
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