使用深度学习预测COVID-19和医院占用率的新型成本效益方法
Nabil I Ajali-Hernández1, Carlos M Travieso-González2
1Signals and Communications Department (DSC), University of Las Palmas de Gran Canaria, Campus Universitario de Tafira, 35017, Las Palmas de Gran Canaria, Spain. nabil.ajali101@alu.ulpgc.es.
Scientific reports
|October 30, 2024
概括
这项研究开发了使用长短期记忆 (LSTM) 和双向LSTM (BiLSTM) 层来准确预测COVID-19演变的系统. 该模型以低计算成本提供可靠的长期流行病预测,有助于医疗保健决策.
科学领域:
- 流行病学 流行病学
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 随着COVID-19大流行,全球医疗保健系统受到严重压力,死亡率上升,并凸显了对预测工具的需求.
- 危机期间有效的医疗保健管理需要准确预测疾病传播和资源需求.
研究的目的:
- 开发和实施一个准确的,低成本的预测系统,用于预测流行病的演变,包括COVID-19病例和医院占用率.
- 通过可靠的长期预测,改善医疗保健管理中的决策.
主要方法:
- 使用互连的长短期内存 (LSTM) 与双双向的LSTM (BiLSTM) 层.
- 实施了一种基于未来时间窗口的新预处理技术.
- 在40%的数据上训练模型,实现准确的长期预测.
主要成果:
- 该预测系统在预测COVID-19病例和医院占用率方面表现出准确性.
- 取得了改善的平均绝对误差 (MAE) <161,根平均平方误差 (RMSE) <405,平均绝对百分比误差 (MAPE) >0.20.
- 在接下来的三天里,每天都能用最少的数据对案件进行查询.
结论:
- 开发的LSTM-BiLSTM模型为医疗保健系统提供了一个强大的工具,为战略规划提供了有价值的见解.
- 该系统支持优化医疗保健和经济资源的分配,改善公共卫生结果.
- 这种方法提供了一个具有成本效益的解决方案,用于准确的,长期的流行病预测.
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