在大数据时代构建合成人口
Mioara A Nicolaie1, Koen Füssenich2, Caroline Ameling3
1Centre for Nutrition, Prevention and Health Services, RIVM (National Institute for Public Health and the Environment), P.O. Box 1, Mailbox 86, 3720 BA, Bilthoven, The Netherlands. alina.nicolaie@rivm.nl.
Population health metrics
|November 1, 2023
概括
本研究介绍了一种用于公共卫生模型的合成人口数据的方法. 合成数据准确地反映了原始人口,同时保证了机密性,解决了访问敏感健康信息的限制.
科学领域:
- 公共卫生 公共卫生
- 计算流行病学计算流行病学
- 数据科学数据科学数据科学
背景情况:
- 开发公共卫生干预模型需要大量的机密个人数据 (社会人口统计,经济,健康).
- 对于开源微型模拟模型来说,这种敏感数据并不容易获得.
- 现有的数据收集方法往往缺乏必要的规模和保密性,以实现现实的建模.
研究的目的:
- 提出一种用于构建高质量的合成人口数据的新方法.
- 克服获取详细个人健康和社会经济数据的机密性障碍.
- 为了使公共卫生干预的微模拟模型的使用.
主要方法:
- 将行政记录和健康登记数据与整个荷兰人口的社会经济和生活方式因素联系起来.
- 利用回归建模来顺序预测个体特征,并结合健康监测调查中的生活方式数据.
- 应用了一种序列预测方法来归因缺失的个体特征.
主要成果:
- 生成的合成人群与原来的机密数据集非常相似.
- 在序列过程中早期预测的特征与原始数据具有很高的相似性.
- 后期阶段的预测表现出由于数据质量和先前建模的累积限制.
结论:
- 开发的方法成功地构建了一个大规模的,高质量的合成群体.
- 这种方法有效地解决了与敏感的健康和社会经济数据相关的机密性问题.
- 合成数据是公共卫生研究和干预建模的宝贵资源.
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