从现实世界基因组测试数据中使用遗传祖先来推算种族和种族类别
Brooke Rhead1, Paige E Haffener, Yannick Pouliot
1Tempus Labs, Inc., Chicago, IL, 60654, USA*Joint first authorship.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|December 31, 2023
概括
在现实世界数据 (RWD) 中缺少种族和种族数据限制了医疗保健公平研究. 这项研究开发了使用遗传祖先的新方法,以准确地归纳这些缺失的信息,改善RWD的实用性.
科学领域:
- 基因组学就是基因组学.
- 医疗信息学 医疗信息学
- 在瘤学瘤学.
背景情况:
- 在真实世界数据 (RWD) 中不完整的种族和种族数据阻碍了解决医疗保健差异的努力.
- 准确的人口统计信息对于理解和减轻患者护理中的不平等至关重要.
研究的目的:
- 开发和验证使用遗传祖先数据归因种族和种族的方法.
- 增强RWD在研究癌症护理中的种族差异方面的实用性.
主要方法:
- 开发了两种归算方法:一种是启发式的,一种是基于机器学习的.
- 利用了超过10万名癌症患者的非识别瘤分析数据.
- 与基于地理位置和姓名的方法进行归算性能比较.
主要成果:
- 两种开发的方法都超过了现有的地理位置和基于姓氏的方法.
- 机器学习方法在四个种族/种族类别中显示出高回忆率 (0.859-0.993) 和精度 (0.932-0.981).
- 在一大群癌症患者中,成功地从遗传祖先中归因种族和种族.
结论:
- 使用基因祖先的新方法可以有效地将RWD中的种族和种族归咎于RWD.
- 这种方法为改善RWD人口统计数据的完整性提供了一条途径.
- 加强RWD可以更好地支持对医疗保健和癌症治疗中的种族差异的研究.
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