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B Leader

Showing results (41-50 of 73) with videos related to

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Plos One|November 12, 2020
Electronic health record analysis identifies kidney disease as the leading risk factor for hospitalization in confirmed COVID-19 patientsMatthew T Oetjens, Jonathan Z Luo, Alexander Chang, et al.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing|November 30, 2016
IDENTIFYING GENETIC ASSOCIATIONS WITH VARIABILITY IN METABOLIC HEALTH AND BLOOD COUNT LABORATORY VALUES: DIVING INTO THE QUANTITATIVE TRAITS BY LEVERAGING LONGITUDINAL DATA FROM AN EHRShefali S Verma, Anastasia M Lucas, Daniel R Lavage, et al.
Circulation|May 9, 2022
rECHOmmend: An ECG-Based Machine Learning Approach for Identifying Patients at Increased Risk of Undiagnosed Structural Heart Disease Detectable by EchocardiographyAlvaro E Ulloa-Cerna, Linyuan Jing, John M Pfeifer, et al.
European Heart Journal|August 7, 2019
Routinely reported ejection fraction and mortality in clinical practice: where does the nadir of risk lie?Gregory J Wehner, Linyuan Jing, Christopher M Haggerty, et al.
Journal of Electrocardiology|November 27, 2022
An ECG-based machine learning model for predicting new-onset atrial fibrillation is superior to age and clinical features in identifying patients at high stroke riskSushravya Raghunath, John M Pfeifer, Christopher R Kelsey, et al.
Circulation. Genomic and Precision Medicine|October 23, 2019
Prevalence and Electronic Health Record-Based Phenotype of Loss-of-Function Genetic Variants in Arrhythmogenic Right Ventricular Cardiomyopathy-Associated GenesEric D Carruth, Wilson Young, Dominik Beer, et al.
Diabetes|December 15, 2019
Clinical and Molecular Prevalence of Lipodystrophy in an Unascertained Large Clinical Care CohortClaudia Gonzaga-Jauregui, Wenzhen Ge, Jeffrey Staples, et al.
NPJ Digital Medicine|July 16, 2019
Finding missed cases of familial hypercholesterolemia in health systems using machine learningJuan M Banda, Ashish Sarraju, Fahim Abbasi, et al.
JACC. Heart Failure|May 11, 2020
A Machine Learning Approach to Management of Heart Failure PopulationsLinyuan Jing, Alvaro E Ulloa Cerna, Christopher W Good, et al.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing|January 19, 2016
INTEGRATING CLINICAL LABORATORY MEASURES AND ICD-9 CODE DIAGNOSES IN PHENOME-WIDE ASSOCIATION STUDIESAnurag Verma, Joseph B Leader, Shefali S Verma, et al.
Pageof 8

Showing results (41-50 of 73) with videos related to

Sort By:
Pageof 8
Plos One|November 12, 2020
Electronic health record analysis identifies kidney disease as the leading risk factor for hospitalization in confirmed COVID-19 patientsMatthew T Oetjens, Jonathan Z Luo, Alexander Chang, et al.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing|November 30, 2016
IDENTIFYING GENETIC ASSOCIATIONS WITH VARIABILITY IN METABOLIC HEALTH AND BLOOD COUNT LABORATORY VALUES: DIVING INTO THE QUANTITATIVE TRAITS BY LEVERAGING LONGITUDINAL DATA FROM AN EHRShefali S Verma, Anastasia M Lucas, Daniel R Lavage, et al.
Circulation|May 9, 2022
rECHOmmend: An ECG-Based Machine Learning Approach for Identifying Patients at Increased Risk of Undiagnosed Structural Heart Disease Detectable by EchocardiographyAlvaro E Ulloa-Cerna, Linyuan Jing, John M Pfeifer, et al.
European Heart Journal|August 7, 2019
Routinely reported ejection fraction and mortality in clinical practice: where does the nadir of risk lie?Gregory J Wehner, Linyuan Jing, Christopher M Haggerty, et al.
Journal of Electrocardiology|November 27, 2022
An ECG-based machine learning model for predicting new-onset atrial fibrillation is superior to age and clinical features in identifying patients at high stroke riskSushravya Raghunath, John M Pfeifer, Christopher R Kelsey, et al.
Circulation. Genomic and Precision Medicine|October 23, 2019
Prevalence and Electronic Health Record-Based Phenotype of Loss-of-Function Genetic Variants in Arrhythmogenic Right Ventricular Cardiomyopathy-Associated GenesEric D Carruth, Wilson Young, Dominik Beer, et al.
Diabetes|December 15, 2019
Clinical and Molecular Prevalence of Lipodystrophy in an Unascertained Large Clinical Care CohortClaudia Gonzaga-Jauregui, Wenzhen Ge, Jeffrey Staples, et al.
NPJ Digital Medicine|July 16, 2019
Finding missed cases of familial hypercholesterolemia in health systems using machine learningJuan M Banda, Ashish Sarraju, Fahim Abbasi, et al.
JACC. Heart Failure|May 11, 2020
A Machine Learning Approach to Management of Heart Failure PopulationsLinyuan Jing, Alvaro E Ulloa Cerna, Christopher W Good, et al.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing|January 19, 2016
INTEGRATING CLINICAL LABORATORY MEASURES AND ICD-9 CODE DIAGNOSES IN PHENOME-WIDE ASSOCIATION STUDIESAnurag Verma, Joseph B Leader, Shefali S Verma, et al.
Pageof 8