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ID: 4347362 FROM PREDICTION TO INTERVENTION: MACHINE LEARNING IDENTIFIES MODIFIABLE DRIVERS OF AF PROGRESSION FOR CLINICAL ACTION
0
Zitationen
25
Autoren
2025
Jahr
Abstract
Atrial fibrillation (AF) commonly progresses from paroxysmal to persistent and permanent forms, a transition that is associated with substrate development and is linked to worse clinical outcomes yet remains poorly addressed in routine practice. Identifying the modifiable risk factors associated with this progression is critical for prevention, especially in underserved populations such as the Louisiana cohort.
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Autoren
- Mohammad Montaser Atasi
- Yishi Jia
- Chanho Lim
- S M Hubbard
- Ghassan Bidaoui
- Mayana Bsoul
- Hadi Younes
- Abboud Hassan
- Michel Abou Khalil
- Christian Massad
- Yara Menassa
- Yingshuo Liu
- Duo Yu
- Eli Tsakiris
- Joe Abi‐Rached
- Hongyu Miao
- Xiang Li
- Estelle Taki
- Charbel Noujaim
- Charles Campbell
- Amitabh C. Pandey
- Swati Rao
- Omar Kreidieh
- Feng Han
- Nassir F. Marrouche