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在多站点EHR数据中的异质选择偏差下增强隐私的顺序学习.

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    科学领域:

    • 生物统计学 生物统计学
    • 流行病学 流行病学
    • 医疗信息学 医疗信息学

    背景情况:

    • 电子健康记录 (EHR) 数据对于疾病研究至关重要,但往往在不同招聘策略的机构之间孤立.
    • 由于数据异质性和隐私问题,集中分析是不可行的,这阻碍了大规模的协作研究.
    • 开发增强隐私的方法对于利用分布式EHR数据至关重要.

    研究的目的:

    • 开发和验证增强隐私的统计方法,以估计多个EHR站点的疾病风险模型参数,采用异质选择.
    • 在不分享个人级别原始数据的情况下实现协作研究,解决隐私和数据访问挑战.
    • 将这些方法应用于吸烟和癌症亚型的跨生物库分析.

    主要方法:

    • 提出了两个分散的顺序估计器:顺序伪概率 (SPL) 和顺序增强反向概率权重 (SAIPW).
    • 利用外部人口层面的信息来调整选择偏差并确保有效的差异估计.
    • 通过模拟将SPL和SAIPW与现有方法 (SUW,集中式,元学习) 进行比较,并将其应用于密歇根基因组学倡议 (MGI) 和NIH我们所有人 (AOU) 的统一数据.

    主要成果:

    • 序列未加权 (SUW) 估计器在模拟中显示出显著的偏差和差的覆盖率.
    • SPL和SAIPW提供了具有有效覆盖的公正估计,SAIPW证明了对选择模型错误规范的稳定性.
    • 分散方法的效率与集中方法相比较,在较小的队列中表现优于元学习.
    • 真实数据分析证实了肺癌,膀癌和喉癌的强烈吸烟与癌症相关性.

    结论:

    • 开发的框架在异构的EHR队列中促进了有效的,增强隐私的统计推断.
    • 实现可扩展,分散的研究,利用现实世界的数据,同时保持个人隐私.
    • 支持在多个地点的研究中对疾病风险关联的可靠估计.