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预测性医疗保健建模用于早期的流行病评估,利用深度自动回归神经先知

Sujata Dash¹, Sourav Kumar Giri², Saurav Mallik³

¹Nagaland University, Dimapur, 797112, Nagaland, India.

Scientific reports

|March 4, 2024

概括

新的预测框架NeuralProphet (NP) 使用神经网络模块改进了流行病预测. 与传统方法相比,这种先进的模型大大减少了预测错误,有助于对传染病的实时决策.

科学领域:

流行病学流行病学
计算生物学计算生物学
数据科学数据科学数据科学

背景情况:

流行病预测的准确性对于公共卫生响应至关重要.
像Prophet这样的现有模型在捕捉复杂的时间序列动态方面存在局限性.
需要可解释和增强的预测工具显而易见.

研究的目的:

介绍NeuralProphet (NP),一种可解释的混合模块化框架,用于流行病预测.
通过集成自动回归 (AR) 和滞后回归 (LR) 神经网络模块来提高预测性能.
使用COVID-19数据,将增强型NP与先知模型的性能进行比较.

主要方法:

使用深度自动回归神经网络 (Deep-AR-Net) 实现AR和LR模块.
使用AdamW和Huber损失函数优化增强的NP框架.
多变量多步时间序列预测在COVID-19数据集上得到验证.

主要成果:

与Prophet.相比,增强的NP显示了与Prophet.相比,平均绝对缩放误差 (MASE) 的显著降低.
综合元件分析显示,印度的长期预测中,AR模块减少了34.7%,LR模块减少了53.4%.
在5个国家,Deep-AR-Net模型将NP的预测误差平均降低了49.21% (短期) 和46.07% (长期).

结论:

通过其集成的神经网络模块,NeuralProphet提供了卓越的流行病预测性能.
该框架提供了比Prophet更准确的预测曲线,更接近实际案例数据.
增强的NP适用于开发用于管理高度传染性疾病的实时决策系统.

关键词:

自动回归网络的自动回归器.深度学习是一种深度学习.滞后的回归器 - 滞后的回归器神经预言者的神经预言者先知是先知,先知就是先知,先知就是先知.短期预测短期预测

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