Chip Shaw

Project Personnel, Texas Tech University Health Sciences Center

6 active projects

Young Adults and Mental Health Comorbidities

Developing one or more research questions about mental health conditions, comorbidities, and health disparities as part of my doctorate program in public health and studying as an NIH All of Us Scholar.

Scientific Questions Being Studied

Developing one or more research questions about mental health conditions, comorbidities, and health disparities as part of my doctorate program in public health and studying as an NIH All of Us Scholar.

Project Purpose(s)

  • Disease Focused Research (major depressive disorder and comorbidities with other health conditions.)
  • Population Health
  • Educational

Scientific Approaches

(To be determined with the help of my All of Us Mentor or through consultation with the faculty at the University of Texas Health Sciences Center School of Public Health.)

Anticipated Findings

(To be determined with the help of my All of Us Mentor or through consultation with the faculty at the University of Texas Health Sciences Center School of Public Health.)

Demographic Categories of Interest

  • Race / Ethnicity
  • Age
  • Geography
  • Disability Status
  • Access to Care

Data Set Used

Registered Tier

Research Team

Owner:

  • Sharon Munroe - Graduate Trainee, University of Texas Health Science Center, Houston
  • Elif Dede Yildirim - Early Career Tenure-track Researcher, Auburn University
  • Chip Shaw - Project Personnel, Texas Tech University Health Sciences Center

Duplicate of Duplicate of Phenotype - Ischemic Heart Disease (v6)

The Notebooks in this workspace can be used to implement well-known phenotype algorithms in one’s own research.

Scientific Questions Being Studied

The Notebooks in this workspace can be used to implement well-known phenotype algorithms in one’s own research.

Project Purpose(s)

  • Educational
  • Methods Development
  • Other Purpose (This is an All of Us Phenotype Library Workspace created by the Researcher Workbench Support team. It is meant to demonstrate the implementation of key phenotype algorithms within the All of Us Research Program cohort.)

Scientific Approaches

Not Applicable

Anticipated Findings

By reading and running the Notebooks in this Phenotype Library Workspace, researchers can implement the following phenotype algorithms:

Christianne L. Roumie; Jana Shirey-Rice, Sunil Kripalani. Vanderbilt University. MidSouth CDRN - Coronary Heart Disease Algorithm. PheKB; 2014. Available from https://phekb.org/phenotype/234

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Chip Shaw - Project Personnel, Texas Tech University Health Sciences Center

Duplicate of Phenotype - Breast Cancer (v6)

The Notebooks in this Workspace can be used to implement well-known phenotype algorithms in one’s own research.

Scientific Questions Being Studied

The Notebooks in this Workspace can be used to implement well-known phenotype algorithms in one’s own research.

Project Purpose(s)

  • Educational
  • Methods Development
  • Other Purpose (This is an All of Us Phenotype Library Workspace created by the Researcher Workbench Support team. It is meant to demonstrate the implementation of key phenotype algorithms within the All of Us Research Program cohort.)

Scientific Approaches

Not Applicable

Anticipated Findings

By reading and running the Notebooks in this Phenotype Library Workspace, researchers can implement the following phenotype algorithms:

Ning Shang, George Hripcsak, Chunhua Weng, Wendy K. Chung, & Katherine Crew. Breast Cancer. Retrieved from https://phekb.org/phenotype/breast-cancer.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Chip Shaw - Project Personnel, Texas Tech University Health Sciences Center

Duplicate of Data fundamentals: python

This notebook will demonstrate the python data analysis methods. This will include an overview of what data is available, how to retrieve data from Electronic Health Record (EHR), Physical Measurements (PM), and Survey data.

Scientific Questions Being Studied

This notebook will demonstrate the python data analysis methods. This will include an overview of what data is available, how to retrieve data from Electronic Health Record (EHR), Physical Measurements (PM), and Survey data.

Project Purpose(s)

  • Educational
  • Methods Development

Scientific Approaches

This Tutorial Workspace contains a Jupyter Notebook written in python. The notebook is divided into the following sections: 1. Setup: How to set up this notebook, install and import software packages, and select the correct version of the CDR. 2. Data Availability Part 1: How to summarize the number of unique participants with major data types: Physical Measurements, Survey, and EHR; 3. Data Availability Part 2: How to delve a little deeper into data availability within each major data type; 4. Data Organization: An explanation of how data is organized according to our common data model. 5. Example Queries: How to directly query the CDR, using two examples of SQL queries to extract demographic data. 6. Expert Tip: How to access the base version of the CDR, for users that want to do their own cleaning.

Anticipated Findings

By reading and running the notebooks in this Tutorial Workspace, you will understand the following: All of Us data are made available in a Curated Data Repository. Participants may contribute any combination of survey, physical measurement, and electronic health record data. Not all participants contribute all possible data types. Each unique piece of health information is given a unique identifier called a concept_id and organized into specific tables according to our common data model. You can use these concept_ids to query the CDR and pull data on specific health information relevant to your analysis. See our support article Learning the Basics of the All of Us Dataset for more info.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Chip Shaw - Project Personnel, Texas Tech University Health Sciences Center

Duplicate of Duplicate of How to Get Started with Registered Tier Data (tier 5)

We recommend that all researchers explore the notebooks in this workspace to learn the basics of All of Us Program Data. What should you expect? This notebook will give you an overview of what data is available in the current…

Scientific Questions Being Studied

We recommend that all researchers explore the notebooks in this workspace to learn the basics of All of Us Program Data.

What should you expect? This notebook will give you an overview of what data is available in the current Curated Data Repository (CDR). It will also teach you how to retrieve information about Electronic Health Record (EHR), Physical Measurements (PM), and Survey data.

Project Purpose(s)

  • Educational
  • Methods Development
  • Other Purpose (This is an All of Us Tutorial Workspace. It is meant to provide instruction for key Researcher Workbench components and All of Us data representation.)

Scientific Approaches

This Tutorial Workspace contains two Jupyter Notebooks (one written in Python, the other in R). Each notebook is divided into the following sections:

1. Setup: How to set up this notebook, install and import software packages, and select the correct version of the CDR.
2. Data Availability Part 1: How to summarize the number of unique participants with major data types: Physical Measurements, Survey, and EHR;
3. Data Availability Part 2: How to delve a little deeper into data availability within each major data type;
4. Data Organization: An explanation of how data is organized according to our common data model.
5. Example Queries: How to directly query the CDR, using two examples of SQL queries to extract demographic data.
6. Expert Tip: How to access the base version of the CDR, for users that want to do their own cleaning.

Anticipated Findings

By reading and running the notebooks in this Tutorial Workspace, you will understand the following:

All of Us data are made available in a Curated Data Repository. Participants may contribute any combination of survey, physical measurement, and electronic health record data. Not all participants contribute all possible data types. Each unique piece of health information is given a unique identifier called a concept_id and organized into specific tables according to our common data model. You can use these concept_ids to query the CDR and pull data on specific health information relevant to your analysis. See our support article Learning the Basics of the All of Us Dataset for more info.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Chip Shaw - Project Personnel, Texas Tech University Health Sciences Center

Data fundamentals: python

This notebook will demonstrate the python data analysis methods. This will include an overview of what data is available, how to retrieve data from Electronic Health Record (EHR), Physical Measurements (PM), and Survey data.

Scientific Questions Being Studied

This notebook will demonstrate the python data analysis methods. This will include an overview of what data is available, how to retrieve data from Electronic Health Record (EHR), Physical Measurements (PM), and Survey data.

Project Purpose(s)

  • Educational
  • Methods Development

Scientific Approaches

This Tutorial Workspace contains a Jupyter Notebook written in python. The notebook is divided into the following sections: 1. Setup: How to set up this notebook, install and import software packages, and select the correct version of the CDR. 2. Data Availability Part 1: How to summarize the number of unique participants with major data types: Physical Measurements, Survey, and EHR; 3. Data Availability Part 2: How to delve a little deeper into data availability within each major data type; 4. Data Organization: An explanation of how data is organized according to our common data model. 5. Example Queries: How to directly query the CDR, using two examples of SQL queries to extract demographic data. 6. Expert Tip: How to access the base version of the CDR, for users that want to do their own cleaning.

Anticipated Findings

By reading and running the notebooks in this Tutorial Workspace, you will understand the following: All of Us data are made available in a Curated Data Repository. Participants may contribute any combination of survey, physical measurement, and electronic health record data. Not all participants contribute all possible data types. Each unique piece of health information is given a unique identifier called a concept_id and organized into specific tables according to our common data model. You can use these concept_ids to query the CDR and pull data on specific health information relevant to your analysis. See our support article Learning the Basics of the All of Us Dataset for more info.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Chip Shaw - Project Personnel, Texas Tech University Health Sciences Center
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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.