机器学习驱动的COVID-19住院预测:从理论到实践在一个主要的东北学术医学中心
Alexander Y Tulchinsky1, Xihan Zhao2, Nodar Kipshidze1
1One Health Trust, Washington, District of Columbia, USA.
Open forum infectious diseases
|June 13, 2025
概括
这项研究引入了一种增强的机器学习模型,用于预测2019年新冠病毒病 (COVID-19) 住院治疗情况,显示了准确度的显著改善. 该模型有助于医院在资源分配和疫情防控方面做好准备.
科学领域:
- 流行病学 流行病学
- 机器学习 机器学习
- 公共卫生 公共卫生
背景情况:
- 准确预测呼吸道病毒浪潮,包括COVID-19,对公共卫生至关重要.
- 现有的COVID-19预测模型需要性能增强.
研究的目的:
- 开发和评估用于预测COVID-19住院治疗的先进机器学习模型.
- 提高流行病应对预测模型的准确性和实用性.
主要方法:
- 扩展了神经基础扩展分析的时间序列预测 (N-BEATS) 架构.
- 集成的时间卷积网络用于外源变量和剩余块用于概率预测.
- 与COVID-19预测中心组合的性能比较,并在医院环境中实施转移学习.
主要成果:
- 在美国COVID-19住院治疗的加权组合中,平均绝对误差 (MAE) 提高了34.0%.
- 使用平均绝对百分比误差 (MAPE) 和对称平均绝对百分比误差 (sMAPE) 证明了卓越的性能.
- 提供可操作的预测,用于医院资源分配和激增规划.
结论:
- 改进的模型显著改善了COVID-19住院预测,特别是在高峰和复苏时.
- 成功的现实世界实施表明了在呼吸道病毒爆发期间有助于决策的潜力.
- 强调先进机器学习在疫情防控和资源管理方面的价值.
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