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Mohammad R Eissa

Showing results (1-10 of 8) with videos related to

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IEEE Journal of Biomedical and Health Informatics|February 25, 2020
Intelligent Data-Driven Model for Diabetes Diurnal Patterns AnalysisMohammad R Eissa, Tim Good, Jackie Elliott, et al.
IEEE Journal of Biomedical and Health Informatics|January 25, 2022
Blood Glucose Level Prediction: Advanced Deep-Ensemble Learning ApproachHoda Nemat, Heydar Khadem, Mohammad R Eissa, et al.
Computers in Biology and Medicine|March 7, 2022
COVID-19 mortality risk assessments for individuals with and without diabetes mellitus: Machine learning models integrated with interpretation frameworkHeydar Khadem, Hoda Nemat, Mohammad R Eissa, et al.
Talanta|February 20, 2020
Classification before regression for improving the accuracy of glucose quantification using absorption spectroscopyHeydar Khadem, Mohammad R Eissa, Hoda Nemat, et al.
IEEE Journal of Biomedical and Health Informatics|January 25, 2020
A Deep Neural Network Application for Improved Prediction of [Formula: see text] in Type 1 DiabetesAleksandr Zaitcev, Mohammad R Eissa, Zheng Hui, et al.
Frontiers in Clinical Diabetes and Healthcare|May 4, 2023
Automatic inference of hypoglycemia causes in type 1 diabetes: a feasibility studyAleksandr Zaitcev, Mohammad R Eissa, Zheng Hui, et al.
Frontiers in Clinical Diabetes and Healthcare|June 23, 2023
Corrigendum: Automatic inference of hypoglycemia causes in type 1 diabetes: a feasibility studyAleksandr Zaitcev, Mohammad R Eissa, Zheng Hui, et al.
Diabetic Medicine : a Journal of the British Diabetic Association|October 9, 2022
Analysis of real-world capillary blood glucose data to help reduce HbA<sub>1c</sub> and hypoglycaemia in type 1 diabetes: Evidence in favour of using the percentage of readings in target and coefficient of variationMohammad R Eissa, Mohammed Benaissa, Tim Good, et al.
Pageof 1

Showing results (1-10 of 8) with videos related to

Sort By:
Pageof 1
IEEE Journal of Biomedical and Health Informatics|February 25, 2020
Intelligent Data-Driven Model for Diabetes Diurnal Patterns AnalysisMohammad R Eissa, Tim Good, Jackie Elliott, et al.
IEEE Journal of Biomedical and Health Informatics|January 25, 2022
Blood Glucose Level Prediction: Advanced Deep-Ensemble Learning ApproachHoda Nemat, Heydar Khadem, Mohammad R Eissa, et al.
Computers in Biology and Medicine|March 7, 2022
COVID-19 mortality risk assessments for individuals with and without diabetes mellitus: Machine learning models integrated with interpretation frameworkHeydar Khadem, Hoda Nemat, Mohammad R Eissa, et al.
Talanta|February 20, 2020
Classification before regression for improving the accuracy of glucose quantification using absorption spectroscopyHeydar Khadem, Mohammad R Eissa, Hoda Nemat, et al.
IEEE Journal of Biomedical and Health Informatics|January 25, 2020
A Deep Neural Network Application for Improved Prediction of [Formula: see text] in Type 1 DiabetesAleksandr Zaitcev, Mohammad R Eissa, Zheng Hui, et al.
Frontiers in Clinical Diabetes and Healthcare|May 4, 2023
Automatic inference of hypoglycemia causes in type 1 diabetes: a feasibility studyAleksandr Zaitcev, Mohammad R Eissa, Zheng Hui, et al.
Frontiers in Clinical Diabetes and Healthcare|June 23, 2023
Corrigendum: Automatic inference of hypoglycemia causes in type 1 diabetes: a feasibility studyAleksandr Zaitcev, Mohammad R Eissa, Zheng Hui, et al.
Diabetic Medicine : a Journal of the British Diabetic Association|October 9, 2022
Analysis of real-world capillary blood glucose data to help reduce HbA<sub>1c</sub> and hypoglycaemia in type 1 diabetes: Evidence in favour of using the percentage of readings in target and coefficient of variationMohammad R Eissa, Mohammed Benaissa, Tim Good, et al.
Pageof 1