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基于机器学习的预测用于使用多基因风险评分对中风患者进行分类

Marco Piazzi, Yifei Wang, Andrew Hornback

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 3, 2025

概括

将遗传风险得分与临床数据相结合,可以显著改善中风预测模型. 这种方法增强了对高风险个体的早期识别,以实现个性化预防策略.

科学领域:

遗传学和基因组学遗传学和基因组学
生物统计学和流行病学
医疗保健中的机器学习

背景情况:

脑卒中是全球主要的健康问题,导致严重的残疾和死亡.
虽然非遗传 (NG) 因素至关重要,但遗传倾向也会影响中风易感性.
早期识别高风险个体是有效预防中风的关键.

研究的目的:

评估多基因风险评分 (PRS) 在机器学习 (ML) 模型中对中风风险预测的附加值.
为了比较使用PRS加NG因子和单独使用NG因子的ML模型的预测准确度.
为了确定影响中风风险预测转移的因素,与PRS集成.

主要方法:

开发了ML模型,利用英国生物库数据预测10年内发生的中风风险.
综合多基因风险评分 (PRS) 与传统的非遗传 (NG) 临床变量.
优化模型用于使用AUROC的预测准确性,分析变量影响.

主要成果:

结合PRS的ML模型表明,与单独NG因素相比,对中风风险的预测准确度提高.
分析显示,在包括PRS后,预测中风概率发生了变化.
确定了影响模型预测的关键人口统计,生物标志物和临床因素.

结论:

多基因风险评分为基于ML的中风预测提供了非遗传因素的显著附加值.
将PRS与NG因素一起纳入常规实践,可以改善早期中风诊断和患者的治疗结果.
对特定队列和可变相互作用的进一步研究可以完善个性化中风预防.

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