Artificial intelligence MacHIne learning for the detection and treatment of atrial fibrillation guidelines in the emergency department setting (AIM HIGHER): Assessing a machine learning clinical decision support tool to detect and treat non‐valvular atrial fibrillation in the emergency department

OBJECTIVE: Advanced machine learning technology provides an opportunity to improve clinical electrocardiogram (ECG) interpretation, allowing non‐cardiology clinicians to initiate care for atrial fibrillation (AF). The Lucia Atrial Fibrillation Application (Lucia App) photographs the ECG to determine...

Descripción completa

Detalles Bibliográficos
Autores principales: Schwab, Kim, Nguyen, Dacloc, Ungab, GilAnthony, Feld, Gregory, Maisel, Alan S., Than, Martin, Joyce, Laura, Peacock, W. Frank
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
The Practice of Emergency Medicine
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8353018/
https://www.ncbi.nlm.nih.gov/pubmed/34401870
http://dx.doi.org/10.1002/emp2.12534

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8353018/
https://www.ncbi.nlm.nih.gov/pubmed/34401870
http://dx.doi.org/10.1002/emp2.12534

Ejemplares similares