深度学习模型使用日历,气象,互联网搜索数据和股票市场价格来预测紧急部门的访问
Chua Ming1, Geraldine Jw Lee2, Yao Neng Teo1
1Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
Computer methods and programs in biomedicine
|May 2, 2025
概括
预测急诊室 (ED) 患者数量可以通过结合当地股市数据和谷歌趋势来改进. 长期短期记忆 (LSTM) 模型显示出强大的预测能力,每天ED访问.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 预测分析是一种预测分析.
背景情况:
- 准确预测急诊室 (ED) 患者数量对于优化医院资源配置至关重要,包括人员和床位管理.
- 互联网搜索数据和股市价格已经显示出预测ED数量和疾病模式的潜力.
- 之前的研究表明,股票市场表现和ED患者访问之间存在积极的相关性.
研究的目的:
- 评估整合互联网搜索数据和股票市场指数与传统日历和气象数据的有效性,以增强基于深度学习的ED患者数量预测.
- 为了确定混合深度学习架构是否与标准模型相比提供更高的预测性能.
主要方法:
- 采用深度学习模型,包括长短期记忆 (LSTM),1D卷积神经网络 (1D CNN) 和混合CNN-LSTM架构.
- 利用各种输入变量:日历数据,气象数据,谷歌趋势在线搜索数据和股票市场指数 (S&P 500和STI).
- 2010-2012年新加坡总医院每日预测ED患者数量,使用这些输入变量的排列.
主要成果:
- 纳入海峡时报指数 (STI) 显著提高了CNN-LSTM混合模型的性能.
- 添加特定的谷歌趋势搜索术语提高了五个混合模型中的两个模型的预测准确度.
- 性能最好的LSTM模型在所有预测器组合中实现了4.8672%的平均绝对百分比误差 (MAPE).
结论:
- 长期短期记忆 (LSTM) 模型对每日急诊室患者数量表现出强大的预测能力.
- 当地的股票市场指数为估计ED访问量提供了可行的预测指标.
- 仅凭日历和气象数据就为相对准确的ED访问预测提供了基础.
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