从行政健康记录生成合成数据,用于药物安全性和有效性研究
Olawale F Ayilara1, Robert W Platt2, Matt Dahl3
1Department of Community Health Sciences, University of Manitoba, Winnipeg, Canada.
International journal of population data science
|February 28, 2024
概括
使用ModOSIM生成合成行政健康记录 (AHR) 紧密模仿真实世界的数据,证明对药物安全研究和培训有价值. 这种方法有助于开发分析方法和快速实施研究协议.
科学领域:
- 医疗信息学 医疗信息学
- 药物监督 药物监督 药物监督
- 数据科学数据科学数据科学
背景情况:
- 行政健康记录 (AHR) 对于基于人群的药物安全性和比较有效性研究至关重要.
- 诸如数据提取成本,隐私问题和批准延迟等挑战阻碍了使用真实世界AHR的及时研究.
- 合成AHR为开发分析方法和培训数据分析师提供了可行的解决方案.
研究的目的:
- 使用两种不同的方法生成和比较合成的行政健康记录 (AHRs) 与现实世界的AHRs.
- 评估合成AHR在药物安全性研究的方法研究和分析师培训中的实用性.
主要方法:
- 来自加拿大曼尼托巴省 (2008-2017) 的现实世界AHR被使用,其中包含处方药记录.
- 合成数据是使用观察医学数据集模拟器II (OSIM2) 和修改版 (ModOSIM) 来生成的.
- 通过使用处方药使用的频率,百分比和一致系数来比较数据.
主要成果:
- 通过ModOSIM生成的合成数据显示,与OSIM2 (16%) 相比,对药物使用平均持续时间的真实数据的一致性 (88%) 较高.
- 虽然性别和年龄分布相似,但与现实数据相比,OSIM2和ModOSIM在药物记录和每人独一无二的药物方面存在显著差异.
- 通过多项措施,ModOSIM与人口研究数据库 (PRDR) 显示出更大的相似性.
结论:
- ModOSIM方法有效地生成了与现实数据非常相似的合成AHR.
- 合成的AHR有助于加快方法研究的实施和加强数据分析师培训.
- 这种方法促进了有效的药物安全研究和医疗保健决策.
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