通过使用不准确的电子健康记录数据进行联合半监督转移学习来提高遗传风险预测
Yuying Lu1, Tian Gu1, Rui Duan2
1Department of Biostatistics, Columbia Mailman School of Public Health, New York, NY 10032, USA.
Statistics in biosciences
|September 8, 2025
概括
联合半监督转移学习 (FEST) 通过使用多样化的数据,改善了代表性不足的群体的疾病风险预测. 这种方法提高了不同人群的医疗保健公平性和模型准确性.
科学领域:
- 基因组学就是基因组学.
- 机器学习 机器学习
- 生物信息学是一种生物信息学.
背景情况:
- 基因组学和电子健康记录 (EHR) 提供了个性化的医疗潜力.
- 缺乏"黄金标准"疾病标签和人口不平衡阻碍了机器学习模型的开发.
- 公正的医疗保健解决方案需要解决数据差异.
研究的目的:
- 引入FEderated半监督转移学习 (FEST) 以提高疾病风险预测.
- 使用协作,多样化的数据,改善代表性不足的人群的预测.
- 解决人口和机构之间的分布差异.
主要方法:
- 利用来自不同亚群的标记和未标记数据进行协作模型培训.
- 使用密度比重重和模型校准来管理分布变化.
- 利用联合学习进行总结级统计培训.
主要成果:
- 与替代方法相比,模拟研究表明FEST的疗效优于其他方法.
- FEST成功地训练了一种2型糖尿病遗传风险预测模型,用于非洲祖先人口.
- 现实世界的数据应用证实了FEST的增强性能.
结论:
- 在代表性不足的人群中,FEST有效地改善了疾病风险预测.
- 该方法解决了基因组学和EHR中的数据挑战.
- FEST促进了公平,准确的个性化医疗.
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