Paulina Paul
Project Personnel, University of California, San Diego
4 active projects
Systemic Disease and Glaucoma
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.
Data Set Used
Registered TierResearch Team
Owner:
- Tsung-Ting Kuo - Early Career Tenure-track Researcher, University of California, San Diego
- Sally Baxter - Research Fellow, University of California, San Diego
- Roxana Loperena Cortes - Other, All of Us Program Operational Use
- Paulina Paul - Project Personnel, University of California, San Diego
- Lucila Ohno-Machado
- Luca Bonomi - Project Personnel, Vanderbilt University Medical Center
- Jihoon Kim - Project Personnel, University of California, San Diego
- Bharanidharan Radha Saseendrakumar - Project Personnel, University of California, San Diego
Collaborators:
- Sophia Sidhu - Graduate Trainee, University of California, San Diego
- Francis Ratsimbazafy - Other, All of Us Program Operational Use
- Nghia Nguyen - Research Fellow, University of California, San Diego
- Gordon Ye - Undergraduate Student, University of California, San Diego
- Bonnie Huang - Graduate Trainee, Northwestern University
- Arash Delavar - Graduate Trainee, University of California, San Diego
- Suad Alshammari - Graduate Trainee, Virginia Commonwealth University
- Silas Contaifer - Graduate Trainee, Virginia Commonwealth University
- Kerry Goetz - Project Personnel, National Institutes of Health (NIH)
- Joshua Morriss - Graduate Trainee, Virginia Commonwealth University
- VIRGINIA UNIVERSITY - Graduate Trainee, Virginia Commonwealth University
- Alireza Kamalipour - Research Fellow, University of California, San Diego
Personal and Family History of Cancer
Scientific Questions Being Studied
As a demonstration project, we seek to understand family history characteristics in prevalence of cancer. Our questions are: 1. How does prevalence of cancer differ between those with and without family history of breast, colorectal, lung, ovarian and prostate cancer; and 2) What, if any, differences exist by demographic characteristics.
Project Purpose(s)
- Disease Focused Research (cancer)
- Population Health
- Other Purpose (This work is a result of an All of Us Research Program Demonstration Project. The projects are efforts by the Program designed to meet the program's goal of ensuring the quality and utility of the Research Hub as a resource for accelerating discovery in science and medicine. This work was reviewed and overseen by the All of Us Research Program Science Committee and the Data and Research Center to ensure compliance with program policy, including policies for acceptable data access and use.)
Scientific Approaches
We analyze all types of cancer in adults and compare analyses to published literature such as studies using SEER national cancer registry and national surveys such as National Health Information Survey. We identify cancer cases based on self-report from the PPI individual medical history survey. We identify family history of cancer from the PPI family medical history survey. We use Jupyter notebooks to generate reusable code.
Anticipated Findings
We anticipate that we will be able to replicate the relative prevalence and family history of cancer seen in the literature. This will serve to demonstrate the quality and utility of All of Us data and tools for conducting epidemiologic analyses.
Demographic Categories of Interest
- Race / Ethnicity
- Age
- Sex at Birth
- Gender Identity
- Sexual Orientation
Data Set Used
Registered TierResearch Team
Owner:
- Paulina Paul - Project Personnel, University of California, San Diego
- Lauryn Bruce - Graduate Trainee, University of California, San Diego
- Katherine Kim - Early Career Tenure-track Researcher, University of California, Davis
- Jihoon Kim - Project Personnel, University of California, San Diego
Collaborators:
- Jun Qian - Other, All of Us Program Operational Use
- Jennifer Zhang - Project Personnel, All of Us Program Operational Use
- Ashley Green - Project Personnel, All of Us Program Operational Use
Systemic Disease and Glaucoma (Cloned)
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.
Data Set Used
Registered TierResearch Team
Owner:
- Tsung-Ting Kuo - Early Career Tenure-track Researcher, University of California, San Diego
- Sally Baxter - Research Fellow, University of California, San Diego
- Roxana Loperena Cortes - Other, All of Us Program Operational Use
- Francis Ratsimbazafy - Other, All of Us Program Operational Use
- Paulina Paul - Project Personnel, University of California, San Diego
- Melissa Patrick - Project Personnel, All of Us Program Operational Use
- Lucila Ohno-Machado
- Luca Bonomi - Project Personnel, Vanderbilt University Medical Center
- Kelsey Mayo - Other, All of Us Program Operational Use
- Jihoon Kim - Project Personnel, University of California, San Diego
- Bharanidharan Radha Saseendrakumar - Project Personnel, University of California, San Diego
- Ashley Able - Other, All of Us Program Operational Use
Collaborators:
- Chenjie Zeng - Research Fellow, National Human Genome Research Institute (NIH-NHGRI)
Old Duplicate of Systemic Disease and Glaucoma
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.
Data Set Used
Registered TierResearch Team
Owner:
- Tsung-Ting Kuo - Early Career Tenure-track Researcher, University of California, San Diego
- Sally Baxter - Research Fellow, University of California, San Diego
- Roxana Loperena Cortes - Other, All of Us Program Operational Use
- Paulina Paul - Project Personnel, University of California, San Diego
- Lucila Ohno-Machado
- Luca Bonomi - Project Personnel, Vanderbilt University Medical Center
- Katherine Kim - Early Career Tenure-track Researcher, University of California, Davis
- Jihoon Kim - Project Personnel, University of California, San Diego
- Bharanidharan Radha Saseendrakumar - Project Personnel, University of California, San Diego
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