整合多基因风险改善了疾病轨迹的生成预测
Chris German1, Suyash Shringarpure2, Payam Dibaeinia2
123andMe, Inc., Palo Alto, CA, USA, chrisg@23andme.com.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|February 27, 2026
概括
一个新的AI模型,即下一个健康事件 (NHE) 模型,通过整合遗传数据和健康史来预测未来的疾病. 预测健康的这一进步为预测个体疾病途径提供了一个强大的新框架.
科学领域:
- 医疗信息学医学信息学
- 医疗保健中的人工智能
- 基因组学和个性化医学
背景情况:
- 预测终身疾病序列是一个重大的医疗挑战.
- 现有的人工智能模型受到队列大小和缺乏遗传数据集成的限制.
- 生成模型为改善健康轨迹预测提供了潜力.
研究的目的:
- 介绍下一个健康事件 (NHE) 模型,一个生成变压器.
- 整合人口统计数据,纵向BMI和多基因风险评分 (PRS) 与健康史.
- 提高未来疾病诊断的预测准确度.
主要方法:
- 在710万参与者的健康轨迹上训练了一个变压器架构.
- 纳入人口统计数据,297个特征的纵向BMI和PRS.
- 将NHE模型的性能与XGBoost.com等基线模型进行比较.
主要成果:
- 在129种疾病中,NHE模型在预测下一个诊断时,比XGBoost (22.3%) 实现了更高的Top-1准确率 (25.5%).
- 多基因风险得分和纵向BMI显著促进了预测能力.
- 模型显示了前性与综合性结果报告的一致准确性 (AUROC 0.917).
结论:
- 通过将大规模的健康史与遗传学相结合,NHE模型为预测健康建立了新的框架.
- 生成型模型可以有效地预测个体疾病途径.
- PRS和纵向BMI是关键预测因素,而生活方式信息提供有限的附加价值.
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