Incorporating longitudinal history of risk factors into atherosclerotic cardiovascular disease risk prediction using deep learning

Incorporating longitudinal history of risk factors into atherosclerotic cardiovascular disease risk prediction using deep learning

Abstract It is increasingly clear that longitudinal risk factor levels and trajectories are related to risk for atherosclerotic cardiovascular disease (ASCVD) above and beyond single measures. Currently used in clinical care, the Pooled Cohort Equations (PCE) are based on regression methods that pre...

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Bibliographic Details
Main Authors: Jingzhi Yu, Xiaoyun Yang, Yu Deng, Amy E. Krefman, Lindsay R. Pool, Lihui Zhao, Xinlei Mi, Hongyan Ning, John Wilkins, Donald M. Lloyd-Jones, Lucia C. Petito, Norrina B. Allen
Format: Article
Language:English
Published: Nature Portfolio 2024-01-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-024-51685-5

Internet

https://doi.org/10.1038/s41598-024-51685-5

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