Výsledky vyhledávání - "Alvaro Ulloa Cerna"

Akademický článek

OASIS +: leveraging machine learning to improve the prognostic accuracy of OASIS severity score for predicting in-hospital mortality

Autor: Yasser EL-Manzalawy, Mostafa Abbas, Ian Hoaglund, Alvaro Ulloa Cerna, Thomas B. Morland, Christopher M. Haggerty, Eric S. Hall, Brandon K. Fornwalt

Publikováno v: BMC Medical Informatics and Decision Making, Vol 21, Iss 1, Pp 1-13 (2021)

Abstract Background Severity scores assess the acuity of critical illness by penalizing for the deviation of physiologic measurements from normal and aggregating these penalties (also called “weights” or “subscores”) into a final score (or pr

Externí odkaz: https://doaj.org/article/fd3175879dbf4acc818eac958f6f62cc

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Abstract 9599: Rechommend: An Ecg-Based Machine-Learning Approach for Identifying Patients at High-Risk of Undiagnosed Structural Heart Disease Detectable by Echocardiography

Autor: Alvaro Ulloa Cerna, Linyuan Jing, John Pfeifer, Sushravya Raghunath, Jeffrey Ruhl, Daniel Rocha, Joseph Leader, Noah Zimmerman, Steven R Steinhubl, Greg Lee, Christopher Good, Christopher M Haggerty, Brandon Fornwalt, Ruijun Chen

Publikováno v: Circulation. 144

Introduction: Timely diagnosis of structural heart disease improves patient outcomes, yet millions remain undiagnosed. ECG-based prediction models can help identify high-risk patients for targeted screening, but existing individual disease models oft

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::dbc8a5836456db5fd1fa3c8378754347
https://doi.org/10.1161/circ.144.suppl_1.9599

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Abstract 9756: An ECG-Based Machine Learning Model for Predicting New Onset Atrial Fibrillation is Superior to Age and Clinical Variables in Selecting a Population at High Stroke Risk

Autor: John Pfeifer, Sushravya Raghunath, Christopher Kelsey, Jeffrey Ruhl, Dustin Hartzel, Alvaro Ulloa Cerna, Linyuan Jing, David vanMaanen, Joseph Leader, Thomas Morland, Ruijun Chen, Christoph Griessenauer, Noah Zimmerman, Steven R Steinhubl, Brandon Fornwalt, Christopher M Haggerty

Publikováno v: Circulation. 144

Background: Several large trials have employed age or clinical features to select patients for atrial fibrillation (AF) screening to reduce strokes. We hypothesized that a deep neural network (DNN) model risk prediction based on ECG would be superior

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::c7f25755c06dc065af12b46f39c2b644
https://doi.org/10.1161/circ.144.suppl_1.9756

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