Casey Taylor

Early Career Tenure-track Researcher, Johns Hopkins University

6 active projects

AMIA Genomics Walkthrough - TIRI Lab Sandbox

This workspace is intended to provide basic instruction for using genomics data in All of Us, including cohort identification, covariate extraction, analysis, and display of results.

Scientific Questions Being Studied

This workspace is intended to provide basic instruction for using genomics data in All of Us, including cohort identification, covariate extraction, analysis, and display of results.

Project Purpose(s)

  • Educational

Scientific Approaches

We use OMOP, the cohort builder, and Hail for genomics analysis. The approach will be a simple rule based algorithm along with some open access genomic data to simulate a GWAS.

Anticipated Findings

We do not anticipate any findings as the analysis will not be real. Therefore this will not contribute to scientific knowledge, but hopefully will contribute to individual user's knowledge.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Michelle Nguyen - Graduate Trainee, Johns Hopkins University
  • Casey Taylor - Early Career Tenure-track Researcher, Johns Hopkins University

Collaborators:

  • Shanshan Song - Graduate Trainee, Johns Hopkins University
  • Rebecca Yoo - Graduate Trainee, Johns Hopkins University
  • Nidhi Soley - Project Personnel, Johns Hopkins University

Identifying and assessing risk for recurrent breast cancer - controlled

We are exploring the data to detect patients that have had a recurrent breast cancer, assess the performance of existing tools to predict risk for recurrent breast cancer, and study the value of various data pre-processing strategies to improve performs…

Scientific Questions Being Studied

We are exploring the data to detect patients that have had a recurrent breast cancer, assess the performance of existing tools to predict risk for recurrent breast cancer, and study the value of various data pre-processing strategies to improve performs of tools to predict risk.

Project Purpose(s)

  • Methods Development

Scientific Approaches

First, to detect patients with a recurrent breast cancer, we will revise a published phenotype algorithm to our use case. Second, we will apply published risk assessment algorithms and assess the performance of those algorithms to identify patients at risk for recurrent breast cancer. Last, we will study the impact of strategies to such as imputation methods to improve the performance of risk assessment tools.

Anticipated Findings

We anticipate demonstrating strategies to improve the performance of existing risk models with an All of Us study population.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Casey Taylor - Early Career Tenure-track Researcher, Johns Hopkins University

Collaborators:

  • Rebecca Yoo - Graduate Trainee, Johns Hopkins University

Identifying and assessing risk for recurrent breast cancer

We are exploring the data to detect patients that have had a recurrent breast cancer, assess the performance of existing tools to predict risk for recurrent breast cancer, and study the value of various data pre-processing strategies to improve performs…

Scientific Questions Being Studied

We are exploring the data to detect patients that have had a recurrent breast cancer, assess the performance of existing tools to predict risk for recurrent breast cancer, and study the value of various data pre-processing strategies to improve performs of tools to predict risk.

Project Purpose(s)

  • Methods Development

Scientific Approaches

First, to detect patients with a recurrent breast cancer, we will revise a published phenotype algorithm to our use case. Second, we will apply published risk assessment algorithms and assess the performance of those algorithms to identify patients at risk for recurrent breast cancer. Last, we will study the impact of strategies to such as imputation methods to improve the performance of risk assessment tools.

Anticipated Findings

We anticipate demonstrating strategies to improve the performance of existing risk models with an All of Us study population.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Casey Taylor - Early Career Tenure-track Researcher, Johns Hopkins University

Collaborators:

  • Rebecca Yoo - Graduate Trainee, Johns Hopkins University

Healthcare utilization and disparities

Changes in healthcare access and utilization due to the COVID-19 pandemic may have impacted the health of some groups more than others. The goal of this research is to assess if there are associations between changes in healthcare access/utilization during…

Scientific Questions Being Studied

Changes in healthcare access and utilization due to the COVID-19 pandemic may have impacted the health of some groups more than others. The goal of this research is to assess if there are associations between changes in healthcare access/utilization during the pandemic and health disparities.

Project Purpose(s)

  • Population Health

Scientific Approaches

To detect associations between healthcare access/utilization and health disparities, we will use EHR data and survey data. We will assess changes in health care access/utilization using data from the Health Care Access & Utilization survey. We will explore health disparity outcomes using EHR data and data from the COPE survey. We will also use data from the The Basics survey in order to understand factors that moderate associations we detect.

Anticipated Findings

We anticipate detecting potential health disparities due to changes in healthcare access/utilization during the pandemic that occur disproportionately in some groups over others. This work will contribute to health disparities research by investigating change in healthcare access/utilization as a mechanism underlying disparities.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Casey Taylor - Early Career Tenure-track Researcher, Johns Hopkins University

Postoperative surgery pain management and recovery - Dataset v5

We are exploring the data to identify indicators of postoperative surgery outcomes and preoperative predictors of those outcomes. We will also build predictive models of postoperative outcomes.

Scientific Questions Being Studied

We are exploring the data to identify indicators of postoperative surgery outcomes and preoperative predictors of those outcomes. We will also build predictive models of postoperative outcomes.

Project Purpose(s)

  • Social / Behavioral
  • Methods Development
  • Ancestry

Scientific Approaches

This project will explore the potential to identify indicators of postoperative surgery outcomes and to identify predictors of those outcomes from All of Us data. We plan to use AllofUs EHR data, FitBit data and genotype data for this exploration. If successful, we will build predictive models for various postoperative surgery outcomes (e.g., pain management, recovery time, etc.)

Anticipated Findings

We anticipate demonstrating the value of combining EHR, physical activity monitoring, and genotype data to predict postoperative surgery outcomes.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Casey Taylor - Early Career Tenure-track Researcher, Johns Hopkins University

Collaborators:

  • Shanshan Song - Graduate Trainee, Johns Hopkins University
  • Nidhi Soley - Project Personnel, Johns Hopkins University

Postoperative surgery pain management and recovery

We are exploring the data to identify indicators of postoperative surgery outcomes and preoperative predictors of those outcomes. We will also build predictive models of postoperative outcomes.

Scientific Questions Being Studied

We are exploring the data to identify indicators of postoperative surgery outcomes and preoperative predictors of those outcomes. We will also build predictive models of postoperative outcomes.

Project Purpose(s)

  • Social / Behavioral
  • Methods Development
  • Ancestry

Scientific Approaches

This project will explore the potential to identify indicators of postoperative surgery outcomes and to identify predictors of those outcomes from All of Us data. We plan to use AllofUs EHR data, FitBit data and genotype data for this exploration. If successful, we will build predictive models for various postoperative surgery outcomes (e.g., pain management, recovery time, etc.)

Anticipated Findings

We anticipate demonstrating the value of combining EHR, physical activity monitoring, and genotype data to predict postoperative surgery outcomes.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Casey Taylor - Early Career Tenure-track Researcher, Johns Hopkins University
1 - 6 of 6
<
>
Request a Review of this Research Project

You can request that the All of Us Resource Access Board (RAB) review a research purpose description if you have concerns that this research project may stigmatize All of Us participants or violate the Data User Code of Conduct in some other way. To request a review, you must fill in a form, which you can access by selecting ‘request a review’ below.