健康记录中断,5526个现实世界时间序列,由众包视觉检查标记的变化点
T Phuong Quan1, Ben Lacey2, Tim E A Peto1
1Nuffield Department of Clinical Medicine, University of Oxford, Oxford OX3 9DU, UK.
GigaScience
|July 28, 2023
概括
研究人员创建了一个标记电子健康记录 (EHR) 时间序列的数据集,以改善自动化数据质量检查. 这有助于验证检测现实世界健康数据变化的方法,这对于疾病监测至关重要.
科学领域:
- 医疗信息学 医疗信息学
- 数据科学数据科学数据科学
- 统计方法 统计方法
背景情况:
- 电子健康记录 (EHR) 是有价值的研究数据来源.
- 目前检查电子健康记录数据质量的方法是手动的,耗时的.
- 自动化数据质量检查对于疾病监测和公共仪表板至关重要.
研究的目的:
- 创建一个验证的数据集,用于评估EHR数据中的自动化变化点检测方法.
- 促进可靠的自动化数据质量监测工具的开发.
主要方法:
- 从8个EHR数据集生成了5526个时间序列.
- 聘请了2000多名公民科学家来识别时间序列图中的变化点.
- 通过基于密度的聚类使用共识标签,并通过专家注释进行验证.
主要成果:
- 实现了高性能指标:80.4%的灵敏度,99.8%的特异性,84.5%的正预测值和99.7%的负预测值.
- 在3,687个时间序列中确定了12,745个变化点.
- 创建了一个大集合的标记EHR时间序列用于验证.
结论:
- 标记的EHR时间序列数据集可以在现实环境中验证自动化变化点检测.
- 本资源鼓励开发实用,自动化的数据质量评估方法.
- 数据集的实用性超越了电子健康记录,扩展到需要时间序列分析的其他领域.
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