Gordon Ye

Undergraduate Student, University of California, San Diego

10 active projects

SDHA in Eye Conditions - v6 Dataset

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:

  • Tonya Lee - Graduate Trainee, University of California, San Diego
  • Sophia Sidhu - Graduate Trainee, University of California, San Diego
  • Sally Baxter - Research Fellow, University of California, San Diego
  • Kaela Acuff - Graduate Trainee, University of California, San Diego
  • Gordon Ye - Undergraduate Student, University of California, San Diego
  • Bita Shahrvini - Graduate Trainee, University of California, San Diego
  • Bharanidharan Radha Saseendrakumar - Project Personnel, University of California, San Diego
  • Arash Delavar - Graduate Trainee, University of California, San Diego

Collaborators:

  • Joy Guo - Graduate Trainee, University of California, San Diego
  • Bonnie Huang - Graduate Trainee, Northwestern University
  • Kiana Tavakoli - Research Fellow, University of California, San Diego

Meta-analysis of tobacco use disorders and associations with health outcomes

Tobacco use disorders (TUD) are the most prevalent substance use disorder in the US, with a high proportion of smokers meeting criteria for dependence. These individuals often have difficulty quitting, experience withdrawal symptoms, and continue smoking despite negative mental, social,…

Scientific Questions Being Studied

Tobacco use disorders (TUD) are the most prevalent substance use disorder in the US, with a high proportion of smokers meeting criteria for dependence. These individuals often have difficulty quitting, experience withdrawal symptoms, and continue smoking despite negative mental, social, and medical consequences. Genetic factors influence smoking behaviors and strides have been made in understanding aspects of tobacco initiation and use via genome-wide association studies (GWAS). However existing GWAS of TUD and nicotine-related traits have small sample sizes and focus on individuals of European ancestry. We performed a cross-ancestral meta-analysis of TUD in 740,361 individuals of European, African American, and Latin American ancestries and hope to replicate some of the main findings using All of Us, and evaluate what are the social and medical outcomes associated with genetic liability to TUD.

Project Purpose(s)

  • Disease Focused Research (tobacco use disorders)
  • Ancestry

Scientific Approaches

We will perform genetic associations of loci implicated in our study in the AllOfUs dataset using REGINIE, accounting for relevant covariates and principal components of genetic ancestry. We will test associations with smoking traits as well as other comorbid traits.

Anticipated Findings

This work will furthers our biological understanding of TUD and its shared genetic risk with other mental and physical traits, and establish EHR as useful sources of phenotypic information for studying the genetics of TUD.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Sandra Sanchez-Roige - Early Career Tenure-track Researcher, University of California, San Diego
  • Gordon Ye - Undergraduate Student, University of California, San Diego

Collaborators:

  • John Meredith - Project Personnel, University of California, San Diego

Original - Social Determinants and Healthcare Access in Eye Conditions

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:

  • Terrence Lee - Graduate Trainee, University of California, San Diego
  • Sally Baxter - Research Fellow, University of California, San Diego
  • John McDermott - Graduate Trainee, University of California, San Diego
  • Grace Ahn - Graduate Trainee, University of California, San Diego
  • Gordon Ye - Undergraduate Student, University of California, San Diego
  • Alison Chan - Graduate Trainee, University of California, San Diego
  • Bita Shahrvini - Graduate Trainee, University of California, San Diego
  • Bharanidharan Radha Saseendrakumar - Project Personnel, University of California, San Diego
  • Arash Delavar - Graduate Trainee, University of California, San Diego

Collaborators:

  • Hua Ou - Mid-career Tenured Researcher, National Institutes of Health (NIH)

Social Determinants and Healthcare Access in Eye Conditions - v5 Dataset

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:

  • Terrence Lee - Graduate Trainee, University of California, San Diego
  • Sally Baxter - Research Fellow, University of California, San Diego
  • John McDermott - Graduate Trainee, University of California, San Diego
  • Grace Ahn - Graduate Trainee, University of California, San Diego
  • Gordon Ye - Undergraduate Student, University of California, San Diego
  • Alison Chan - Graduate Trainee, University of California, San Diego
  • Bita Shahrvini - Graduate Trainee, University of California, San Diego
  • Bharanidharan Radha Saseendrakumar - Project Personnel, University of California, San Diego
  • Arash Delavar - Graduate Trainee, University of California, San Diego

Collaborators:

  • Joy Guo - Graduate Trainee, University of California, San Diego
  • Alireza Kamalipour - Research Fellow, University of California, San Diego
  • Priyanka Soe - Project Personnel, University of California, San Diego
  • Mahasweta Nayak - Undergraduate Student, University of California, San Diego
  • Cecilia Vallejos - Undergraduate Student, University of California, San Diego

SDHA in Eye Conditions - v5 Dataset

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:

  • Terrence Lee - Graduate Trainee, University of California, San Diego
  • Tonya Lee - Graduate Trainee, University of California, San Diego
  • Sophia Sidhu - Graduate Trainee, University of California, San Diego
  • Sally Baxter - Research Fellow, University of California, San Diego
  • Kaela Acuff - Graduate Trainee, University of California, San Diego
  • John McDermott - Graduate Trainee, University of California, San Diego
  • Grace Ahn - Graduate Trainee, University of California, San Diego
  • Gordon Ye - Undergraduate Student, University of California, San Diego
  • Alison Chan - Graduate Trainee, University of California, San Diego
  • Bita Shahrvini - Graduate Trainee, University of California, San Diego
  • Bharanidharan Radha Saseendrakumar - Project Personnel, University of California, San Diego
  • Arash Delavar - Graduate Trainee, University of California, San Diego

Collaborators:

  • Francis Ratsimbazafy - Other, All of Us Program Operational Use
  • Jun Qian - Other, All of Us Program Operational Use
  • Joy Guo - Graduate Trainee, University of California, San Diego
  • Bonnie Huang - Graduate Trainee, Northwestern University

Depression Fitbit Study (Clinical Phenotyping)

Major depressive disorder (MDD) is associated with changes in physical activity, fidgeting/restlessness, energy levels, and sleep patterns. It has also been established that most MDD patients have recurrent episodes, separated by periods of "remission" that may last 2 months or…

Scientific Questions Being Studied

Major depressive disorder (MDD) is associated with changes in physical activity, fidgeting/restlessness, energy levels, and sleep patterns. It has also been established that most MDD patients have recurrent episodes, separated by periods of "remission" that may last 2 months or more. The purpose of this study is to assess the ability of the Fitbit data to longitudinally distinguish individuals with and without a depression diagnosis during a transitional phase coded in the medical record (i.e., a new or change in diagnostic code). We will focus primarily on daily physical activity (minutes of activity, steps traveled, elevation traveled), energy expenditure (calories burned), and heart rate.

Project Purpose(s)

  • Disease Focused Research (major depressive disorder)
  • Social / Behavioral

Scientific Approaches

We will build datasets and cohorts of individuals meeting the MDD/depression diagnostic criteria, as well as subsets for severity and additional modifiers. We will perform time series analysis of the Fitbit data with machine learning models, and assess their performance in the above questions. We will look specifically for trends and patterns in the time series Fitbit data that may be unique to the MDD/depression group.

Anticipated Findings

Consumer-grade technology has been pushing into the field of health and wellness. While previous studies have evaluated the performance of wearables in psychiatric and cardiovascular diseases, few have the sample sizes or data collection lengths made possible by the All of Us. We know that MDD comes with it changes in physical activity, and positive results may reinforce the role of wearable technology as a confirmatory step in the diagnostic process. Null results, on the other hand, will highlight the unique challenges of consumer health-related technologies, especially as these devices are becoming more and more common.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Collaborators:

  • Tonya Lee - Graduate Trainee, University of California, San Diego

Problematic Alcohol Use and Related Psychiatric and Cardiometabolic Conditions

The aim of this proposal is to identify new genetic and non-genetic factors that are leveraged through a machine-learning based approach to predict individual 1) risk for problematic alcohol use, 2) outcome from problematic alcohol use (e.g., recurrence of alcohol-related…

Scientific Questions Being Studied

The aim of this proposal is to identify new genetic and non-genetic factors that are leveraged through a machine-learning based approach to predict individual 1) risk for problematic alcohol use, 2) outcome from problematic alcohol use (e.g., recurrence of alcohol-related hospitalization or death), and 3) genetic and non-genetic alcohol risk factors that are shared with other psychiatric diseases and obesity-related cardiometabolic phenotypes. Prediction will utilize a combination of genetic, clinical, and lifestyle risk factors. Ultimately, we aim to identify not only individual predictors, but build a novel risk prediction model that improves on currently developed polygenic risk scores, which show clinical promise, but have methodological shortcomings that limit their accuracy.

Project Purpose(s)

  • Disease Focused Research (alcohol misuse and associated diseases)
  • Social / Behavioral
  • Ancestry

Scientific Approaches

We are integrating data from the UK Biobank and the NIH All of Us Research Program. We will conduct genome-wide studies (GWAS) with REGENIE or Tractor to identify new variants associated with our alcohol use phenotypes. Potential clinical predictors will be identified from literature and machine learning (e.g., gradient boosting) analyses of separate lifestyle domains. For individual risk prediction, we will use neural networks to reduce the dimensionality of genetic data into interpretable independent latent factors, demonstrate that the latent factors recapitulate the alcohol-related GWAS associations that we identified, before finally using the latent factors in a multi-layer perceptron model to predict broad alcohol use phenotypes. We will also use neural networks to predict different sub-components of problematic alcohol use and negative alcohol use outcomes. Similar stepwise approaches will be performed for Mondrian cross-conformal prediction and gradient boosting classification.

Anticipated Findings

There is growing interest in using genetic risk scores in clinical practice, especially for decision making surrounding early intervention in high-risk individuals as well as triaging alcohol- and cardiometabolic-risk after an alcohol-related medical event for increased follow-up. Current genetic risk models can stratify individuals into large buckets of risk, but many of these methods discard useful information in favor of simple models or fail to generalize to real-world settings. A further limitation broadly plaguing the field of genomics research is the drastic overrepresentation of individuals of white European ancestry. By leveraging the diversity of the All of Us Research Program, we seek to build reproducible and equitable risk prediction models that may benefit a diverse patient population.

Demographic Categories of Interest

  • Race / Ethnicity
  • Gender Identity
  • Geography
  • Education Level
  • Income Level

Data Set Used

Controlled Tier

Research Team

Owner:

  • Gordon Ye - Undergraduate Student, University of California, San Diego
  • Eric Zorrilla - Early Career Tenure-track Researcher, Scripps Research

Collaborators:

  • Sandra Sanchez-Roige - Early Career Tenure-track Researcher, University of California, San Diego
  • Emily Zhu - Other, Scripps Research
  • Jennifer Zhang - Project Personnel, All of Us Program Operational Use
  • Eli Browne - Other, Scripps Research

Depression Fitbit Study (PHQ-9 Phenotyping)

Depression is associated with changes in physical activity, fidgeting/restlessness, energy levels, and sleep patterns. It has also been established that most depression patients have recurrent episodes, separated by periods of "remission" that may last 2 months or more. The purpose…

Scientific Questions Being Studied

Depression is associated with changes in physical activity, fidgeting/restlessness, energy levels, and sleep patterns. It has also been established that most depression patients have recurrent episodes, separated by periods of "remission" that may last 2 months or more. The purpose of this study is to assess the ability of longitudinal Fitbit data to identify individuals with worsening symptoms of depression, and those potentially entering a depressive episode using longitudinal PHQ-9 survey responses, a standard and validated depression screening tool. We will focus primarily on daily physical activity (minutes of activity, steps traveled, elevation traveled), energy expenditure (calories burned), and heart rate.

Project Purpose(s)

  • Disease Focused Research (major depressive disorder)
  • Social / Behavioral
  • Educational

Scientific Approaches

We will extract Fitbit data from participants who have responded to the PHQ-9 questions administered as part of the COPE survey. We will first compute PHQ-9 scores to stratify individuals by depression severity (or healthy control status). We will then train and evaluate machine learning models using the Fitbit data prior to PHQ-9 survey completion to predict the onset of worsening depressive symptoms and potential depressive episodes.

Anticipated Findings

Consumer-grade technology including smartwatches has been pushing into the field of health and wellness. While previous studies have evaluated the utility of mobile health (mHealth) technologies, few have the sample sizes or data collection lengths made possible by the All of Us. This is particularly the case for mHealth research in psychiatric diseases: the vast majority focus on data collected from small cohorts that may not be representative of real-world diversity. We know that depression comes with it changes in physical activity, and positive results may reinforce the role of wearable technology as a confirmatory or early detection step in the diagnostic process. Null results, on the other hand, will highlight the unique challenges of consumer health-related technologies despite their adoption by the public.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Collaborators:

  • Prothit Halder - Undergraduate Student, Scripps Research
  • Nolan Chai - Undergraduate Student, University of California, San Diego
  • Natalie Kwong - Undergraduate Student, University of California, San Diego

SDHA in Eye Conditions - v4 Dataset

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:

  • Terrence Lee - Graduate Trainee, University of California, San Diego
  • Sally Baxter - Research Fellow, University of California, San Diego
  • John McDermott - Graduate Trainee, University of California, San Diego
  • Grace Ahn - Graduate Trainee, University of California, San Diego
  • Gordon Ye - Undergraduate Student, University of California, San Diego
  • Alison Chan - Graduate Trainee, University of California, San Diego
  • Bita Shahrvini - Graduate Trainee, University of California, San Diego
  • Bharanidharan Radha Saseendrakumar - Project Personnel, University of California, San Diego
  • Arash Delavar - Graduate Trainee, University of California, San Diego

Social Determinants and Healthcare Access in Eye Conditions - v4 Dataset

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:

  • Terrence Lee - Graduate Trainee, University of California, San Diego
  • Sally Baxter - Research Fellow, University of California, San Diego
  • John McDermott - Graduate Trainee, University of California, San Diego
  • Grace Ahn - Graduate Trainee, University of California, San Diego
  • Gordon Ye - Undergraduate Student, University of California, San Diego
  • Alison Chan - Graduate Trainee, University of California, San Diego
  • Bita Shahrvini - Graduate Trainee, University of California, San Diego
  • Bharanidharan Radha Saseendrakumar - Project Personnel, University of California, San Diego
  • Arash Delavar - Graduate Trainee, University of California, San Diego

Collaborators:

  • Priyanka Soe - Project Personnel, University of California, San Diego
  • Mahasweta Nayak - Undergraduate Student, University of California, San Diego
  • Cecilia Vallejos - Undergraduate Student, University of California, San Diego
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