将风险因素的纵向病史纳入动脉样硬化心血管疾病风险预测中,使用深度学习
Jingzhi Yu1, Xiaoyun Yang1, Yu Deng1
1Northwestern University.
Research square
|October 27, 2023
概括
使用纵向数据的深度学习模型改善了与传统方法相比,动脉样硬化心血管疾病 (ASCVD) 风险预测. 这种方法提高了模型的准确性,以获得更好的患者结果.
科学领域:
- 心血管疾病研究研究
- 人工智能在医学中的应用
- 预测分析是一种预测分析.
背景情况:
- 纵向风险因子数据提供了超出单个措施的动脉样硬化心血管疾病 (ASCVD) 风险预测.
- 当前的聚合队列方程 (PCE) 依赖于横截面数据,限制了它们的预测能力.
- 深度学习 (DL) 模型将纵向数据用于ASCVD风险预测的好处仍然未得到充分探索.
研究的目的:
- 开发和评估深度学习 (DL) 模型,用于使用纵向风险因素数据进行ASCVD风险预测.
- 为了比较DL模型与传统的聚合队列方程 (PCE) 的性能.
主要方法:
- 开发了一个深度学习模型,Dynamic-DeepHit,将8年的纵向风险因素数据纳入其中.
- 这项研究包括来自四个心血管疾病队列的15565名参与者.
- 模型的性能被评估使用歧视 (AUROC) 和校准 (Brier分数) 在一个持久测试数据集,比较DL与PCE.
主要成果:
- 纵向DL模型表现出比PCE更好的性能,其AUROC为0.815 (DL) 与0.792 (PCE).
- 净重新分类指数为0.385,表明风险预测的显著改善.
- 与PCE (0.0542) 相比,DL模型实现了较低的Brier分数 (0.0514).
结论:
- 结合纵向风险因素的深度学习模型显著提高ASCVD风险预测.
- 与传统的PCE相比,开发的DL模型显示出更好的区分和校准.
- 这突显了DL在更准确的心血管风险评估方面的潜力.
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