Tsung-Ting Kuo

Early Career Tenure-track Researcher, University of California, San Diego

10 active projects

Systemic Disease and Glaucoma

We have previously published a predictive model of glaucoma progression using electronic health record (EHR) data pertaining to systemic attributes from a single institution. We aim to use the All of Us dataset to 1) serve as external validation for…

Scientific Questions Being Studied

We have previously published a predictive model of glaucoma progression using electronic health record (EHR) data pertaining to systemic attributes from a single institution. We aim to use the All of Us dataset to 1) serve as external validation for this single-center model and 2) to train new models focused on predicting glaucoma progression using systemic predictors. This is important to understand whether the original findings are generalizable and provide additional knowledge about the utility of systemic predictors on a national-level dataset.

Project Purpose(s)

  • Disease Focused Research (Primary open angle glaucoma)
  • Other Purpose (This work is the result of an All of Us Research Program Demonstration Project. Demonstration Projects are efforts by the All of Us Research Program designed to meet the goal of ensuring the quality and utility of the Research Hub as a resource for accelerating precision medicine. This work has been approved, reviewed, and overseen by the All of Us Research Program Science Committee and Data and Research Center to ensure compliance with program policy. )

Scientific Approaches

We plan to primarily work with EHR data contained in All of Us for a cohort of adult participants diagnosed with primary open-angle glaucoma. We will extract data on systemic conditions and medications for this cohort, as well as physical measurements and vital signs. We will clean the data such that the format is consistent with the data from our previously published model. Then, we will use this data as an external validation of a logistic regression model derived from our prior study that was based at a single academic center. Next, we will use All of Us data to train a new set of models, using techniques such as logistic regression, random forests, and artificial neural networks. We will optimize these models using feature selection methods and class balancing procedures. By evaluating performance metrics such as area under the curve (AUC), precision, recall, and accuracy, we will assess whether we can achieve superior predictive performance when training models using All of Us.

Anticipated Findings

We anticipate that the All of Us data will validate the findings from the model, which demonstrated that blood pressure-related metrics and certain medication classes had predictive value for glaucoma progression. In addition, we anticipate that the models trained with All of Us data will outperform the model trained with single institution data due to larger sample size and greater diversity. These findings will support further investigation in understanding the relationship between systemic conditions like blood pressure with glaucoma progression.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

Collaborators:

  • Francis Ratsimbazafy - Other, All of Us Program Operational Use
  • Nghia Nguyen - Research Fellow, University of California, San Diego
  • Joshua Morriss - Graduate Trainee, Virginia Commonwealth University

Systemic Disease and Glaucoma (Cloned)

We have previously published a predictive model of glaucoma progression using electronic health record (EHR) data pertaining to systemic attributes from a single institution. We aim to use the All of Us dataset to 1) serve as external validation for…

Scientific Questions Being Studied

We have previously published a predictive model of glaucoma progression using electronic health record (EHR) data pertaining to systemic attributes from a single institution. We aim to use the All of Us dataset to 1) serve as external validation for this single-center model and 2) to train new models focused on predicting glaucoma progression using systemic predictors. This is important to understand whether the original findings are generalizable and provide additional knowledge about the utility of systemic predictors on a national-level dataset.

Project Purpose(s)

  • Disease Focused Research (Primary open angle glaucoma)
  • Other Purpose (This work is the result of an All of Us Research Program Demonstration Project. Demonstration Projects are efforts by the All of Us Research Program designed to meet the goal of ensuring the quality and utility of the Research Hub as a resource for accelerating precision medicine. This work has been approved, reviewed, and overseen by the All of Us Research Program Science Committee and Data and Research Center to ensure compliance with program policy. )

Scientific Approaches

We plan to primarily work with EHR data contained in All of Us for a cohort of adult participants diagnosed with primary open-angle glaucoma. We will extract data on systemic conditions and medications for this cohort, as well as physical measurements and vital signs. We will clean the data such that the format is consistent with the data from our previously published model. Then, we will use this data as an external validation of a logistic regression model derived from our prior study that was based at a single academic center. Next, we will use All of Us data to train a new set of models, using techniques such as logistic regression, random forests, and artificial neural networks. We will optimize these models using feature selection methods and class balancing procedures. By evaluating performance metrics such as area under the curve (AUC), precision, recall, and accuracy, we will assess whether we can achieve superior predictive performance when training models using All of Us.

Anticipated Findings

We anticipate that the All of Us data will validate the findings from the model, which demonstrated that blood pressure-related metrics and certain medication classes had predictive value for glaucoma progression. In addition, we anticipate that the models trained with All of Us data will outperform the model trained with single institution data due to larger sample size and greater diversity. These findings will support further investigation in understanding the relationship between systemic conditions like blood pressure with glaucoma progression.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

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

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

(Not Working) 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

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

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

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

Test

What is the prevalence of various social determinants of health and barriers to care for patients with glaucoma in the NIH All of Us Dataset?

Scientific Questions Being Studied

What is the prevalence of various social determinants of health and barriers to care for patients with glaucoma in the NIH All of Us Dataset?

Project Purpose(s)

  • Disease Focused Research (Glaucoma)

Scientific Approaches

I plan to use the NIH All of Us database to describe the prevalence of various social determinants of health and barriers to care for patients with glaucoma.

Anticipated Findings

I will describe various social determinants of health and barriers to care for patients with glaucoma, which I believe will inform practitioners and future researchers using this dataset of the most prevalent barriers that the glaucoma patients may face.

Demographic Categories of Interest

  • Race / Ethnicity
  • Access to Care
  • Education Level
  • Income Level

Research Team

Owner:

Depression Fitbit Study

Major depressive disorder (MDD) is known to result in 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…

Scientific Questions Being Studied

Major depressive disorder (MDD) is known to result in 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 of 1) longitudinally distinguish individuals based on diagnostic severity, and 2) longitudinally distinguish between additional modifiers. We will focus primarily on daily activity (steps) data, and sleep data (once available).

Project Purpose(s)

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

Scientific Approaches

We will build datasets and cohorts of individuals meeting the MDD 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 group, or subsets.

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, none 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.

Research Team

Owner:

Evaluating the Role of Potential Neuroprotective Agents in Glaucoma

Glaucoma, a chronic neurodegenerative disease of retinal ganglion cells (RGCs), is a leading cause of irreversible blindness worldwide. Its management currently focuses on lowering intraocular pressure to slow disease progression. However, disease-modifying, neuroprotective treatments for glaucoma remain a major unmet…

Scientific Questions Being Studied

Glaucoma, a chronic neurodegenerative disease of retinal ganglion cells (RGCs), is a leading cause of irreversible blindness worldwide. Its management currently focuses on lowering intraocular pressure to slow disease progression. However, disease-modifying, neuroprotective treatments for glaucoma remain a major unmet need. Several studies have been performed demonstrating potential for "repurposing" existing medications for protecting RGCs and mitigating the risk of developing glaucoma and/or slowing the progression of glaucoma. However, many of these studies have been performed in basic science settings using animal models and lack large-scale human data for validation. In this workspace, we plan to explore existing data on a diverse nationwide cohort to further evaluate the potential validity of candidate neuroprotective medications for influencing risk of glaucoma.

Project Purpose(s)

  • Disease Focused Research (Glaucoma)

Scientific Approaches

We will primarily employ methods in clinical epidemiology, biostatistics, and machine learning. For questions relating to risk of developing glaucoma, we will examine data from a general older adult population and investigate whether use of candidate medications decreases the risk of developing glaucoma. For questions relating to risk of progressing glaucoma, we will examine data from a cohort of participants with existing diagnoses of glaucoma and evaluate whether use of candidate medications increases the risk of diagnosis codes related to greater severity. Depending on cohort size and the specific medication of interest, we may use cohort study or case-control study approaches. To examine associations of risk, we may use regression modeling, chi-squared analyses, longitudinal modeling, or machine learning methods, depending on the available cohort sizes and the characteristics of the predictor and outcome variables.

Anticipated Findings

We anticipate that this work will provide some validation of findings from the basic science literature by using real-world clinical data to support findings in animal models. Some of the medications may prove to be supported by human data, whereas others may not. Furthermore, we may even identify novel candidate medications not previously identified in the literature. This will advance the scientific knowledge of novel therapeutics for the prevention and treatment of glaucoma.

Demographic Categories of Interest

  • Age

Research Team

Owner:

Old Duplicate of Systemic Disease and Glaucoma

We have previously published a predictive model of glaucoma progression using electronic health record (EHR) data pertaining to systemic attributes from a single institution. We aim to use the All of Us dataset to 1) serve as external validation for…

Scientific Questions Being Studied

We have previously published a predictive model of glaucoma progression using electronic health record (EHR) data pertaining to systemic attributes from a single institution. We aim to use the All of Us dataset to 1) serve as external validation for this single-center model and 2) to train new models focused on predicting glaucoma progression using systemic predictors. This is important to understand whether the original findings are generalizable and provide additional knowledge about the utility of systemic predictors on a national-level dataset.

Project Purpose(s)

  • Disease Focused Research (primary open angle glaucoma)
  • Other Purpose (This work is the result of an All of Us Research Program Demonstration Project. Demonstration Projects are efforts by the All of Us Research Program designed to meet the goal of ensuring the quality and utility of the Research Hub as a resource for accelerating precision medicine. This work has been approved, reviewed, and overseen by the All of Us Research Program Science Committee and Data and Research Center to ensure compliance with program policy. )

Scientific Approaches

We will develop predictive models using the All of Us dataset using multivariable logistic regression, random forests, and artificial neural networks.

Anticipated Findings

We anticipate that the All of Us data will validate the findings from the model, which demonstrated that blood pressure-related metrics and certain medication classes had predictive value for glaucoma progression. In addition, we anticipate that the models trained with All of Us data will outperform the model trained with single institution data due to larger sample size and greater diversity.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

Duplicate (latest ver, for testing) of Systemic Disease and Glaucoma

We have previously published a predictive model of glaucoma progression using electronic health record (EHR) data pertaining to systemic attributes from a single institution. We aim to use the All of Us dataset to 1) serve as external validation for…

Scientific Questions Being Studied

We have previously published a predictive model of glaucoma progression using electronic health record (EHR) data pertaining to systemic attributes from a single institution. We aim to use the All of Us dataset to 1) serve as external validation for this single-center model and 2) to train new models focused on predicting glaucoma progression using systemic predictors. This is important to understand whether the original findings are generalizable and provide additional knowledge about the utility of systemic predictors on a national-level dataset.

Project Purpose(s)

  • Disease Focused Research (Primary open angle glaucoma)
  • Other Purpose (This work is the result of an All of Us Research Program Demonstration Project. Demonstration Projects are efforts by the All of Us Research Program designed to meet the goal of ensuring the quality and utility of the Research Hub as a resource for accelerating precision medicine. This work has been approved, reviewed, and overseen by the All of Us Research Program Science Committee and Data and Research Center to ensure compliance with program policy. )

Scientific Approaches

We plan to primarily work with EHR data contained in All of Us for a cohort of adult participants diagnosed with primary open-angle glaucoma. We will extract data on systemic conditions and medications for this cohort, as well as physical measurements and vital signs. We will clean the data such that the format is consistent with the data from our previously published model. Then, we will use this data as an external validation of a logistic regression model derived from our prior study that was based at a single academic center. Next, we will use All of Us data to train a new set of models, using techniques such as logistic regression, random forests, and artificial neural networks. We will optimize these models using feature selection methods and class balancing procedures. By evaluating performance metrics such as area under the curve (AUC), precision, recall, and accuracy, we will assess whether we can achieve superior predictive performance when training models using All of Us.

Anticipated Findings

We anticipate that the All of Us data will validate the findings from the model, which demonstrated that blood pressure-related metrics and certain medication classes had predictive value for glaucoma progression. In addition, we anticipate that the models trained with All of Us data will outperform the model trained with single institution data due to larger sample size and greater diversity. These findings will support further investigation in understanding the relationship between systemic conditions like blood pressure with glaucoma progression.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

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