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

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

Abstract 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 d...

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Bibliographic Details
Main Authors: Kim Schwab, Dacloc Nguyen, GilAnthony Ungab, Gregory Feld, Alan S. Maisel, Martin Than, Laura Joyce, W. Frank Peacock
Format: Article
Language:English
Published: Wiley 2021-08-01
Series:Journal of the American College of Emergency Physicians Open
Subjects:
artificial intelligence
atrial fibrillation
clinical decision support
emergency department
guidelines
machine learning
Online Access:https://doi.org/10.1002/emp2.12534

Internet

https://doi.org/10.1002/emp2.12534

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