Islam Ebeid
Research Fellow, University of North Texas
3 active projects
Type 2 Diabetes Subtyping
Scientific Questions Being Studied
The effects of different blood components on glucose levels in hyperglycemic patients. We want to create a predictive model which can estimate change in level of glucose by changing blood component concentration, such as RBC counts, electrolyte concentration, etc. , in Type 2 diabetes patients.
Project Purpose(s)
- Disease Focused Research (hyperglycemia)
Scientific Approaches
We will analyze the lab results of 150,000 plus individuals in the All Of Us Research workbench. We will try to find a pattern between glucose levels compared to blood components levels in normal and diabetic patients in Python and determine which blood component is associated with change in blood glucose levels. Once we determine these components, we want to create a predictive model which will determine which blood component can assist in blood glucose maintenance.
Anticipated Findings
The results of our study, once published, will assist doctors in making better decisions to regulate glucose levels with drugs, dietary, and lifestyle changes as well as if any medication changes levels of any blood components, our model can predict how it will affect glucose levels in the patients.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Registered TierResearch Team
Owner:
- Yashu Vashishath - Graduate Trainee, University of North Texas
- Serdar Bozdag - Mid-career Tenured Researcher, University of North Texas
- Islam Ebeid - Research Fellow, University of North Texas
- Mohammad Al Olaimat - Graduate Trainee, University of North Texas
Tier 5 - Type 2 Diabetes Subtyping
Scientific Questions Being Studied
The effects of different blood components on glucose levels in hyperglycemic patients. We want to create a predictive model which can estimate change in level of glucose by changing blood component concentration, such as RBC counts, electrolyte concentration, etc. , in Type 2 diabetes patients.
Project Purpose(s)
- Disease Focused Research (hyperglycemia)
Scientific Approaches
We will analyze the lab results of 150,000 plus individuals in the All Of Us Research workbench. We will try to find a pattern between glucose levels compared to blood components levels in normal and diabetic patients in Python and determine which blood component is associated with change in blood glucose levels. Once we determine these components, we want to create a predictive model which will determine which blood component can assist in blood glucose maintenance.
Anticipated Findings
The results of our study, once published, will assist doctors in making better decisions to regulate glucose levels with drugs, dietary, and lifestyle changes as well as if any medication changes levels of any blood components, our model can predict how it will affect glucose levels in the patients.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Registered TierResearch Team
Owner:
- Yashu Vashishath - Graduate Trainee, University of North Texas
- Serdar Bozdag - Mid-career Tenured Researcher, University of North Texas
- Sarah Beaver - Graduate Trainee, University of North Texas
- Islam Ebeid - Research Fellow, University of North Texas
- Mohammad Al Olaimat - Graduate Trainee, University of North Texas
Person Disease Prediction Network
Scientific Questions Being Studied
The main research question here is wether it is possible to predict disease diagnosis based on relational multi-modal features encoded as latent variables. The primary goal of this research is to predict disease diagnosis and susceptibility using machine learning models by integrating individual data with disease data and deriving relationships between both tiers to be able to predict individual-level disease diagnosis.
Project Purpose(s)
- Population Health
Scientific Approaches
To integrate multimodal datasets we use multiplex heterogeneous networks. A multiplex heterogeneous network is a graph of multiple types of nodes and edges. We then plan to utilize a graph-based machine learning model to predict the potential of an individual at risk of developing a disease.
Anticipated Findings
There are some existing methods that can predict patient diagnosis utilizing biomedical datasets. Those works are specific to a particular disease, are not interpretable, and do not include relational features. This project is a unique study on a large and diverse population where several data types will be integrated together to predict current and future diagnoses of individuals. This method is not disease specific.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Controlled TierRequest a Review of this Research Project
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