预测COVID-19住院情况:X型疾病研究的混合智能方法
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Updated: May 27, 2025
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A Data-Driven Approach to Quantifying Immune States in Sepsis
Published on: February 7, 2025
135
1School of Artificial Intelligence, Suzhou Chien-Shiung Institute of Technology, Suzhou, Jiangsu, China.
概括
预测COVID-19住院治疗对于公共卫生至关重要. 一个新的混合模型,EMD&LSTM-ARIMA,准确预测患者数量,帮助资源管理在流行病如疾病X.
科学领域:
- 流行病学 流行病学
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
背景情况:
- COVID-19大流行凸显了对新出现的传染病做好准备的必要性,例如世卫组织的"X型疾病".
- 监测住院患者数量对于有效的公共卫生反应和资源分配至关重要.
研究的目的:
- 引入一种新的混合智能模型,EMD&LSTM-ARIMA,用于预测COVID-19住院治疗情况.
- 在多个国家评估该模型的预测准确性和实用性.
主要方法:
- 该EMD&LSTM-ARIMA模型集成了经验模式分解 (EMD),长短期记忆 (LSTM) 网络和自动回归集成移动平均线 (ARIMA).
- EMD将数据分解为内在模式函数,LSTM捕获非线性依赖关系,ARIMA模型线性趋势.
- 该模型是使用来自英国,加拿大,意大利和日本的COVID-19住院数据进行训练和测试的.
主要成果:
- 在所有测试国家中,EMD&LSTM-ARIMA模型表现出强的预测性能.
- 所有预测的错误率都低于10%,平均绝对百分比错误 (MAPE) 为2.30% (英国),3.33% (加拿大),1.63% (意大利) 和2.89% (日本).
结论:
- 拟议的EMD&LSTM-ARIMA模型为预测COVID-19住院预测提供了一种强大而准确的方法.
- 这种方法可以显著帮助公共卫生机构在传染病爆发期间管理资源.
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