rECHOmmend: An ECG-Based Machine Learning Approach for Identifying Patients at Increased Risk of Undiagnosed Structural Heart Disease Detectable by Echocardiography

BACKGROUND: Timely diagnosis of structural heart disease improves patient outcomes, yet many remain underdiagnosed. While population screening with echocardiography is impractical, ECG-based prediction models can help target high-risk patients. We developed a novel ECG-based machine learning approac...

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Detalles Bibliográficos
Autores principales: Ulloa-Cerna, Alvaro E., Jing, Linyuan, Pfeifer, John M., Raghunath, Sushravya, Ruhl, Jeffrey A., Rocha, Daniel B., Leader, Joseph B., Zimmerman, Noah, Lee, Greg, Steinhubl, Steven R., Good, Christopher W., Haggerty, Christopher M., Fornwalt, Brandon K., Chen, Ruijun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2022
Materias:
Original Research Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9241668/
https://www.ncbi.nlm.nih.gov/pubmed/35533093
http://dx.doi.org/10.1161/CIRCULATIONAHA.121.057869

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9241668/
https://www.ncbi.nlm.nih.gov/pubmed/35533093
http://dx.doi.org/10.1161/CIRCULATIONAHA.121.057869

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