为代表和覆盖而进行的计算战略招聘在"我们所有人"研究计划中进行了研究
Victor A Borza1,2, Qingxia Chen3,4, Ellen W Clayton5,6,7
1Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA. victor.a.borza@vanderbilt.edu.
NPJ digital medicine
|July 3, 2025
概括
我们所有人研究计划通过创建多样化的美国健康数据库来增强生物医学研究. 一种新的计算方法改善了参与者招募,增加了队列代表性和覆盖范围.
科学领域:
- 生物医学研究的研究.
- 医疗信息学 医疗信息学
- 人口健康 人口健康
背景情况:
- 大规模的数据库对于促进健康和疾病的理解至关重要.
- 我们所有人的研究计划旨在建立一个综合的数据库,包括多样化的美国健康数据.
- 生物医学研究在历史上对某些人口群体进行了不足的研究.
研究的目的:
- 评估2017年至2022年"我们所有人"研究计划的代表性和覆盖范围.
- 提出和评估一种计算战略招聘方法,以优化队列多样性.
- 评估拟议的招聘方法在提高代表性和覆盖率方面的有效性.
主要方法:
- 对2017-2022年"我们所有人"参与者数据的分析.
- 参与者人口统计数据与美国人口普查比例的比较.
- 开发和模拟优化资源配置的计算招聘策略.
- 在各种条件下对招聘模拟结果的评估.
主要成果:
- 我们所有人研究计划成功地招募了大多数未经研究的群体,这些群体在美国人口普查比例或以上.
- 拟议的计算招聘方法显著提高了队列代表性和覆盖范围.
- 代表性和覆盖率的改善在多个模拟场景中是一致的.
结论:
- 我们所有人的研究计划在招募多样化的人口方面取得了成功.
- 开发的计算招聘策略显示,它有望在大型健康研究中增强参与者多样性.
- 该方法可应用于现实场景,以提高生物医学研究队列的代表性和覆盖率.
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