县增强变压器用于COVID-19国家住院预测预测
Siawpeng Er1, Shihao Yang2, Tuo Zhao3
1H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
Scientific reports
|June 20, 2023
概括
准确的COVID-19住院预测对于资源分配至关重要. 一个新的县增强变压器 (CAT) 模型使用深度学习来预测四周前入院,帮助公共卫生决策.
科学领域:
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
- 流行病学 流行病学
背景情况:
- 随着COVID-19大流行,医疗资源受到压力,需要有效的公共卫生决策.
- 准确预测COVID-19住院情况对于有效的医疗资源分配至关重要.
研究的目的:
- 提出一种新的方法,即县增强变压器 (CAT),用于预测COVID-19住院治疗情况.
- 为所有美国州生成准确的四周前入院预测.
主要方法:
- 利用基于变压器的深度学习模型,一种擅长捕捉时间依赖的自我注意力机制.
- 综合公开可用的数据:COVID-19确诊病例,死亡,住院和家庭收入中位数.
- 开发了一种基于数据的方法,用于时间序列预测.
主要成果:
- 该CAT模型在预测COVID-19住院治疗时表现出强度和可用性.
- 数值实验验证实了模型的准确性和效率.
- 该模型有效地捕捉住住院数据中的短期和长期模式.
结论:
- 县增强式变压器 (CAT) 模型显示出作为医疗资源分配工具的希望.
- 准确的住院预测可以显著改善公共卫生应对策略.
- 这种数据驱动的方法为流行病资源管理提供了一个可扩展的解决方案.
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