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Radwa Elshawi

Showing results (1-10 of 9) with videos related to

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BMC Medical Informatics and Decision Making|July 31, 2019
On the interpretability of machine learning-based model for predicting hypertensionRadwa Elshawi, Mouaz H Al-Mallah, Sherif Sakr
Scientific Reports|January 20, 2022
Exploiting time series of Sentinel-1 and Sentinel-2 to detect grassland mowing events using deep learning with reject regionViacheslav Komisarenko, Kaupo Voormansik, Radwa Elshawi, et al.
Springerplus|June 29, 2016
A distributed query execution engine of big attributed graphsOmar Batarfi, Radwa Elshawi, Ayman Fayoumi, et al.
Scientific Reports|April 16, 2024
FIT calculator: a multi-risk prediction framework for medical outcomes using cardiorespiratory fitness dataRadwa Elshawi, Sherif Sakr, Mouaz H Al-Mallah, et al.
International Journal of Cardiology|January 28, 2019
Predictors of in-hospital length of stay among cardiac patients: A machine learning approachTahani A Daghistani, Radwa Elshawi, Sherif Sakr, et al.
Plos One|April 19, 2018
Using machine learning on cardiorespiratory fitness data for predicting hypertension: The Henry Ford ExercIse Testing (FIT) ProjectSherif Sakr, Radwa Elshawi, Amjad Ahmed, et al.
International Journal of Cardiology|November 20, 2016
The impact of digoxin on mortality in patients with chronic systolic heart failure: A propensity-matched cohort studyMay Al-Khateeb, Waqas T Qureshi, Raed Odeh, et al.
BMC Medical Informatics and Decision Making|December 21, 2017
Comparison of machine learning techniques to predict all-cause mortality using fitness data: the Henry ford exercIse testing (FIT) projectSherif Sakr, Radwa Elshawi, Amjad M Ahmed, et al.
The American Journal of Cardiology|September 28, 2017
Using Machine Learning to Define the Association between Cardiorespiratory Fitness and All-Cause Mortality (from the Henry Ford Exercise Testing Project)Mouaz H Al-Mallah, Radwa Elshawi, Amjad M Ahmed, et al.
Pageof 1

Showing results (1-10 of 9) with videos related to

Sort By:
Pageof 1
BMC Medical Informatics and Decision Making|July 31, 2019
On the interpretability of machine learning-based model for predicting hypertensionRadwa Elshawi, Mouaz H Al-Mallah, Sherif Sakr
Scientific Reports|January 20, 2022
Exploiting time series of Sentinel-1 and Sentinel-2 to detect grassland mowing events using deep learning with reject regionViacheslav Komisarenko, Kaupo Voormansik, Radwa Elshawi, et al.
Springerplus|June 29, 2016
A distributed query execution engine of big attributed graphsOmar Batarfi, Radwa Elshawi, Ayman Fayoumi, et al.
Scientific Reports|April 16, 2024
FIT calculator: a multi-risk prediction framework for medical outcomes using cardiorespiratory fitness dataRadwa Elshawi, Sherif Sakr, Mouaz H Al-Mallah, et al.
International Journal of Cardiology|January 28, 2019
Predictors of in-hospital length of stay among cardiac patients: A machine learning approachTahani A Daghistani, Radwa Elshawi, Sherif Sakr, et al.
Plos One|April 19, 2018
Using machine learning on cardiorespiratory fitness data for predicting hypertension: The Henry Ford ExercIse Testing (FIT) ProjectSherif Sakr, Radwa Elshawi, Amjad Ahmed, et al.
International Journal of Cardiology|November 20, 2016
The impact of digoxin on mortality in patients with chronic systolic heart failure: A propensity-matched cohort studyMay Al-Khateeb, Waqas T Qureshi, Raed Odeh, et al.
BMC Medical Informatics and Decision Making|December 21, 2017
Comparison of machine learning techniques to predict all-cause mortality using fitness data: the Henry ford exercIse testing (FIT) projectSherif Sakr, Radwa Elshawi, Amjad M Ahmed, et al.
The American Journal of Cardiology|September 28, 2017
Using Machine Learning to Define the Association between Cardiorespiratory Fitness and All-Cause Mortality (from the Henry Ford Exercise Testing Project)Mouaz H Al-Mallah, Radwa Elshawi, Amjad M Ahmed, et al.
Pageof 1