Jihoon Kim

Project Personnel, University of California, San Diego

8 active projects

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:

Collaborators:

  • Chenjie Zeng - Research Fellow, NIH

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
  • Suad Alshammari - Graduate Trainee, Virginia Commonwealth University
  • Silas Contaifer - Graduate Trainee, Virginia Commonwealth University
  • Joshua Morriss - Graduate Trainee, Virginia Commonwealth University
  • VIRGINIA UNIVERSITY - Graduate Trainee, Virginia Commonwealth University

Personal and Family History of Cancer

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;…

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

Research 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

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:

Cancer Prevalence and Family History

As a demonstration project, we seek to understand the regional, demographic, and family history characteristics in prevalence and incidence of both solid and hematologic (blood) cancers. Our questions are: 1. How do rates of cancer differ based on the self-report…

Scientific Questions Being Studied

As a demonstration project, we seek to understand the regional, demographic, and family history characteristics in prevalence and incidence of both solid and hematologic (blood) cancers. Our questions are:
1. How do rates of cancer differ based on the self-report (participant provided information, or “PPI”) and electronic health records.
2. Are characteristics related to cancer similar or different among the people represented in All of Us compared to other national cohorts.

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 will analyze all types of cancer in adults to compare incidence (new cases) and prevalence (current levels in the population). We will compare our results with the published information from SEER national cancer registry and national surveys (e.g., National Health Information Survey). We will analyze socio-demographics and geographic differences.

We will identify cancer cases based on self-report from PPI individual medical history survey and from diagnosis codes plus lab results from E.H.R.
We will identify family history of cancer form the PPI family medical history survey.
We will map the cancer categories by physiologic site used for the SEER registry to the SNOMED and ICD codes used in the EHR and to the cancer conditions in the PPI. We use Jupyter notebooks to generate reusable code for the mapping.

Anticipated Findings

For this study, we anticipate we will be able to replicate the relative prevalence and incidence rates of cancer and family history of cancer. 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
  • Education Level
  • Income Level

Research 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

Tutorial - Cancer

Are there differences in cancer incidence and prevalence between SEER and AoU?

Scientific Questions Being Studied

Are there differences in cancer incidence and prevalence between SEER and AoU?

Project Purpose(s)

  • Disease Focused Research (cancer)

Scientific Approaches

Not available.

Anticipated Findings

No significant difference

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Jihoon Kim - Project Personnel, University of California, San Diego

Breast Cancer prevalence

Breast Cancer prevalence

Scientific Questions Being Studied

Breast Cancer prevalence

Project Purpose(s)

  • Disease Focused Research (breast cancer)

Scientific Approaches

Not available.

Anticipated Findings

Breast Cancer prevalence

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Jihoon Kim - Project Personnel, University of California, San Diego

breast cancer demo

How breast cancer phenotypes can implemented in AoU

Scientific Questions Being Studied

How breast cancer phenotypes can implemented in AoU

Project Purpose(s)

  • Educational

Scientific Approaches

Not available.

Anticipated Findings

breast cancer phenotypes

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Jihoon Kim - Project Personnel, University of California, San Diego
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