Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram
Diagnosing a heart attack requires excessive testing and prolonged observation, which frequently requires hospital admission. Here the authors report a machine learning-based system based exclusively on ECG data that can help clinicians identify 37% more heart attacks during initial screening.
Guardado en:
Autores principales: | Salah Al-Zaiti, Lucas Besomi, Zeineb Bouzid, Ziad Faramand, Stephanie Frisch, Christian Martin-Gill, Richard Gregg, Samir Saba, Clifton Callaway, Ervin Sejdić |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2020
|
Materias: | |
Acceso en línea: | https://doaj.org/article/142323e4d75448858fff5469968e3125 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Electrocardiogram lead selection for intelligent screening of patients with systolic heart failure
por: Yu-An Chiou, et al.
Publicado: (2021) -
AI-Enabled Algorithm for Automatic Classification of Sleep Disorders Based on Single-Lead Electrocardiogram
por: Erdenebayar Urtnasan, et al.
Publicado: (2021) -
Expert consensus document on automated diagnosis of the electrocardiogram: The task force on automated diagnosis of the electrocardiogram in Japan
por: Takao Katoh, et al.
Publicado: (2021) -
A new deep learning algorithm of 12-lead electrocardiogram for identifying atrial fibrillation during sinus rhythm
por: Yong-Soo Baek, et al.
Publicado: (2021) -
Comparison of electrocardiograms (ECG) waveforms and centralized ECG measurements between a simple 6‐lead mobile ECG device and a standard 12‐lead ECG
por: Robert Kleiman, et al.
Publicado: (2021)