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

• 主要な著者: Tomer Gazit (著者), Hanan Mann (著者), Shiri Gaber (著者), Pavel Adamenko (著者), Granit Pariente (著者), Liron Volsky (著者), Amir Dolev (著者), Helena Lyson (著者), Eyal Zimlichman (著者), Jay A. Pandit (著者), Edo Paz (著者)
• フォーマット: 図書
• 出版事項: Frontiers Media S.A., 2024-11-01T00:00:00Z.
• 主題: article
• オンライン･アクセス: 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...