Casey Taylor
Early Career Tenure-track Researcher, Johns Hopkins University
6 active projects
AMIA Genomics Walkthrough - TIRI Lab Sandbox
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 TierResearch 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
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 TierResearch 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
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 TierResearch Team
Owner:
- Casey Taylor - Early Career Tenure-track Researcher, Johns Hopkins University
Collaborators:
- Rebecca Yoo - Graduate Trainee, Johns Hopkins University
Healthcare utilization and disparities
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 TierPostoperative surgery pain management and recovery - Dataset v5
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 TierResearch 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
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 TierRequest a Review of this Research Project
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