评估将国家纵向购物数据整合到呼吸系统疾病预测模型中的价值
Elizabeth Dolan1,2, James Goulding3, Harry Marshall3
1N/LAB, Nottingham University Business School, University of Nottingham, Nottingham, UK. elizabeth.dolan@nottingham.ac.uk.
Nature communications
|November 22, 2023
概括
整合非处方药销售数据显著改善了呼吸道疾病死亡预测. 这种方法增强了具有高度地理细节的公共卫生监测,特别是在高风险时期.
科学领域:
- 公共卫生 公共卫生
- 流行病学 流行病学
- 医疗信息学 医疗信息学
背景情况:
- 随着COVID-19的爆发,人们需要进行强有力的医疗保健规划,特别是在呼吸系统疾病方面.
- 准确预测呼吸道疾病死亡人数对于有效的公共卫生干预和资源分配至关重要.
研究的目的:
- 评估将非处方药销售数据整合到呼吸系统疾病死亡预测模型中的实用性.
- 评估销售数据是否可以提高预测英格兰当地注册死亡人数的准确性.
主要方法:
- 利用机器学习模型 (PADRUS) 从2016年3月到2020年3月结合超过20亿个零售交易.
- 实现了一个AI可解释性工具 (模型类依赖) 来评估变量的重要性.
- 使用销售数据与传统变量 (社会人口统计,天气) 进行模型性能比较.
主要成果:
- 结合药品销售数据的模型在预测呼吸道疾病死亡时显著超过传统模型.
- 在公共健康风险最高的时期,准确性增长 (R平方增加0.09-0.11) 最明显.
- 帕德鲁斯模型在英格兰的314个地方自治区进行了有效的预测.
结论:
- 非处方药销售数据为监测人口呼吸系统健康提供了有价值的实时指标.
- 将销售数据集成到预测模型中可以显著提高当地死亡预测的准确性.
- 这种方法为详细的公共卫生监测和规划提供了一种新方法.
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