Joy Guo

Graduate Trainee, University of California, San Diego

3 active projects

Duplicate of Access/Utilization and Glaucoma Surgery v6

We intend to study the relationship between healthcare access and utilization and glaucoma surgery.

Scientific Questions Being Studied

We intend to study the relationship between healthcare access and utilization and glaucoma surgery.

Project Purpose(s)

  • Disease Focused Research (Primary open angle glaucoma)

Scientific Approaches

We will use machine learning models to understand the relationship between measures of access and utilization and glaucoma surgery. We plan to use logistic regression, artificial neural networks, random forests, and XGBoost.

Anticipated Findings

We hope that our study will better inform the field of ophthalmology of the relationship between access and utilization and real world glaucoma outcomes such as need for surgery.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Joy Guo - Graduate Trainee, University of California, San Diego
  • Arash Delavar - Research Fellow, Baylor College of Medicine

Collaborators:

  • Brian Johnson - Graduate Trainee, University of California, San Diego
  • Bharanidharan Radha Saseendrakumar - Project Personnel, University of California, San Diego

Access/Utilization and Glaucoma Surgery

We intend to study the relationship between healthcare access and utilization and glaucoma surgery.

Scientific Questions Being Studied

We intend to study the relationship between healthcare access and utilization and glaucoma surgery.

Project Purpose(s)

  • Disease Focused Research (Primary open angle glaucoma)

Scientific Approaches

We will use machine learning models to understand the relationship between measures of access and utilization and glaucoma surgery. We plan to use logistic regression, artificial neural networks, random forests, and XGBoost.

Anticipated Findings

We hope that our study will better inform the field of ophthalmology of the relationship between access and utilization and real world glaucoma outcomes such as need for surgery.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Joy Guo - Graduate Trainee, University of California, San Diego
  • Arash Delavar - Research Fellow, Baylor College of Medicine

Collaborators:

  • Brian Johnson - Graduate Trainee, University of California, San Diego
  • Bharanidharan Radha Saseendrakumar - Project Personnel, University of California, San Diego

Periocular Malignancy - Control - Census

We are planning to explore disparities in healthcare access and utilization for patients with eye conditions across different demographic groups. We would like to evaluate risk of developing advanced/severe disease in different eye conditions, and understand how social determinants contribute…

Scientific Questions Being Studied

We are planning to explore disparities in healthcare access and utilization for patients with eye conditions across different demographic groups. We would like to evaluate risk of developing advanced/severe disease in different eye conditions, and understand how social determinants contribute to this risk while adjusting for other known risk factors. We are also interested in understanding the availability of social determinants of health data in this data repository compared to EHR clinical data warehouses alone.

Project Purpose(s)

  • Population Health

Scientific Approaches

We will build cohorts of patients with various eye diseases (i.e. diabetic retinopathy, retinal vein occlusions, glaucoma, etc.). Then we will develop concept sets and extract data on outcomes (i.e. development of complications), as well as predictors including clinical data and social data. We will draw on survey data and EHR data within All of Us. When genomic data and wearable data become available, we are interested in evaluating those data sources as well. We will use statistical modeling and machine learning to generate predictive models.

Anticipated Findings

We anticipate that there may be differential risk for developing complications based on disparities in healthcare access and utilization for patients with eye conditions.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age
  • Geography
  • Disability Status
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Registered Tier

Research Team

Owner:

  • Joy Guo - Graduate Trainee, University of California, San Diego
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