Yashu Vashishath
Graduate Trainee, University of North Texas
7 active projects
Diabetes
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 (type 1 diabetes mellitus)
- Ancestry
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
Controlled TierResearch Team
Owner:
- Yashu Vashishath - Graduate Trainee, University of North Texas
- Sarah Beaver - Graduate Trainee, University of North Texas
Recent: Type 2 Diabetes Subtyping (Tier 6)
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)
- Ancestry
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
- Sarah Beaver - Graduate Trainee, University of North Texas
Duplicate of Phenotype - Type 2 Diabetes (v6)
Scientific Questions Being Studied
The Notebooks in this Workspace can be used to implement well-known phenotype algorithms in one’s own research, using the Controlled Tier Curated Data Repository (CDR).
Project Purpose(s)
- Educational
- Methods Development
- Other Purpose (This is an All of Us Phenotype Library Workspace created by the Researcher Workbench Support team. It is meant to demonstrate the implementation of key phenotype algorithms within the All of Us Research Program cohort, using the Controlled Tier Curated Data Repository (CDR).)
Scientific Approaches
Controlled-tier All of Us cohort data; Jupyter Notebooks, Cohort Builder, Concept Set Selector, Dataset Selector
Anticipated Findings
By reading and running the Notebooks in this Phenotype Library Workspace, researchers can implement the following phenotype algorithms: Jennifer Pacheco and Will Thompson. Northwestern University. Type 2 Diabetes Mellitus. PheKB; 2012 Available from: https://phekb.org/phenotype/18
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Controlled TierType 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
A1C vs RBC count analysis
Scientific Questions Being Studied
We want to explore data to check if changes in RBC count or Hemoglobin conc. have any effect on A1C in normal as well as diabetic patients. The analysis will help understand if RBC count and Hb conc. can play a role in miss diagnosis of Diabetic patients because of A1C levels.
Project Purpose(s)
- Educational
Scientific Approaches
We are trying to check if the hypothesis is true or not before we conduct any wet lab experiments to confirm our hypothesis.
Anticipated Findings
We anticipate that as the RBC count and Hb conc. increases, A1C levels would go down.
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
Disease Prediction
Scientific Questions Being Studied
Comparing Bayesian Deep Learning techniques on disease prediction to regular deep learning models.
Project Purpose(s)
- Educational
Scientific Approaches
Surveys and labs. Bayesian Deep learning, Neural Networks
Anticipated Findings
If bayesian deep learning models provide better prediction when including probability percentage of predicted outcome.
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
- Sarah Beaver - Graduate Trainee, University of North Texas
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