A novel, machine-learning model for prediction of short-term ASCVD risk over 90 and 365 days

• Váldodahkkit: Tomer Gazit (Dahkki), Hanan Mann (Dahkki), Shiri Gaber (Dahkki), Pavel Adamenko (Dahkki), Granit Pariente (Dahkki), Liron Volsky (Dahkki), Amir Dolev (Dahkki), Helena Lyson (Dahkki), Eyal Zimlichman (Dahkki), Jay A. Pandit (Dahkki), Edo Paz (Dahkki)
• Materiálatiipa: Girji
• Almmustuhtton: Frontiers Media S.A., 2024-11-01T00:00:00Z.
• Fáttát: article
• Liŋkkat: Connect to this object online.

BackgroundCurrent atherosclerotic cardiovascular disease (ASCVD) risk assessment tools like the Pooled Cohort Equations (PCEs) and PREVENT™ scores offer long-term predictions but may not effectively drive behavior change. Short-term risk predictions using mobile health (mHealth) data and electronic...