在COVID-19大流行期间评估医疗服务需求的预测模型
Hang Thanh Bui1, Ming Zhao1, Ben Zhe Wang2
1School of Business, University of New South Wales, Canberra, Australia.
Scientific reports
|August 20, 2025
概括
每天的互联网搜索数据可以预测未来对医疗服务的需求. 机器学习模型比传统方法提供更准确的健康资源预测,改善医疗保健规划.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 数字流行病学数字流行病学
- 卫生经济学 卫生经济学
背景情况:
- 医疗保健资源规划传统上依赖于历史数据,这些数据可能无法捕捉实时需求变化.
- 互联网搜索数据为公共卫生问题和服务需求提供了一种新的高频代理.
- 评估先进的预测模型对于优化医疗保健资源配置至关重要.
研究的目的:
- 评估日常互联网搜索数据对医疗服务需求的预测能力.
- 将机器学习和现在预测模型的有效性与健康服务预测的自动回归模型进行比较.
- 确定数字数据和先进分析对空间健康资源部署的有用性.
主要方法:
- 利用了澳大利亚八个州 (2020-2022) 的每日互联网搜索查询量和每月抗抑郁药支出数据.
- 采用机器学习和现在预测技术以及传统的自回归预测模型.
- 分析数据以评估短期医疗服务需求的预测准确性.
主要成果:
- 互联网搜索数据在预测短期医疗服务需求方面具有显著价值.
- 与自动回归模型相比,机器学习模型的平均平方误差较低,这表明预测性能优越.
- 这些发现突显了将数字数据流整合到健康预测中的潜力.
结论:
- 每日互联网搜索数据是对医疗服务需求的有价值的领先指标.
- 机器学习工具提高了医疗服务需求预测的准确性.
- 这些方法可以显著改善不同地理区域的卫生资源的规划和部署.
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