A novel, machine-learning model for prediction of short-term ASCVD risk over 90 and 365 days
Tomer Gazit, Hanan Mann, Shiri Gaber, Pavel Adamenko, Granit Pariente, Liron Volsky, Amir Dolev, Helena Lyson, Eyal Zimlichman, Jay A. Pandit, Edo Paz · 2024
Read A novel, machine-learning model for prediction of short-term ASCVD risk over 90 and 365 days by Tomer Gazit, Hanan Mann, Shiri Gaber, Pavel Adamenko, Granit Pariente, Liron Volsky, Amir Dolev, Helena Lyson, Eyal Zimlichman, Jay A. Pandit, Edo Paz (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11564171.txt.
Description
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 health records (EHRs) could enhance clinical decision-making and patient engagement.
Catalog record
| CREATOR | Tomer Gazit, Hanan Mann, Shiri Gaber, Pavel Adamenko, Granit Pariente, Liron Volsky, Amir Dolev, Helena Lyson, Eyal Zimlichman, Jay A. Pandit, Edo Paz |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 29 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |