Machine learning-based identification of risk-factor signatures for undiagnosed atrial fibrillation in primary prevention and post-stroke in clinical practice

AIMS: Atrial fibrillation (AF) carries a substantial risk of ischemic stroke and other complications, and estimates suggest that over a third of cases remain undiagnosed. AF detection is particularly pressing in stroke survivors. To tailor AF screening efforts, we explored German health claims data...

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Detalles Bibliográficos
Autores principales: Schnabel, Renate B, Witt, Henning, Walker, Jochen, Ludwig, Marion, Geelhoed, Bastian, Kossack, Nils, Schild, Marie, Miller, Robert, Kirchhof, Paulus
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9745664/
https://www.ncbi.nlm.nih.gov/pubmed/35436783
http://dx.doi.org/10.1093/ehjqcco/qcac013

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9745664/
https://www.ncbi.nlm.nih.gov/pubmed/35436783
http://dx.doi.org/10.1093/ehjqcco/qcac013

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