Research Projects Directory

Research Projects Directory

12,398 active projects

This information was updated 7/18/2024

The Research Projects Directory includes information about all projects that currently exist in the Researcher Workbench to help provide transparency about how the Workbench is being used. Each project specifies whether Registered Tier or Controlled Tier data are used.

Note: Researcher Workbench users provide information about their research projects independently. Views expressed in the Research Projects Directory belong to the relevant users and do not necessarily represent those of the All of Us Research Program. Information in the Research Projects Directory is also cross-posted on AllofUs.nih.gov in compliance with the 21st Century Cures Act.

3 projects have 'Breast Cancer Treatment' in the project title
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The Role of Social Determinants of Health for Breast Cancer Treatment

Social determinants of health play a large role in the outcomes of access to care and quality of treatment. The CDC names social determinants of health as education access and quality, health care and quality, neighborhood and built environment, social…

Scientific Questions Being Studied

Social determinants of health play a large role in the outcomes of access to care and quality of treatment. The CDC names social determinants of health as education access and quality, health care and quality, neighborhood and built environment, social and community context, and economic stability. More specifically we want to answer how social determinants of health affect breast cancer diagnosis and treatment.

Project Purpose(s)

  • Social / Behavioral

Scientific Approaches

The approach used for this study would be data analytics. We would try to analyze and make conclusions about why certain people get better access to care, better treatment, or have a higher survival rate. The reason for this could be based on social determinants of health,

Anticipated Findings

The anticipated findings from this study would be that people who have less economic stability, are in rural areas, have worse insurance coverage, and higher education, as well as other SDOHs will have a worse outcome when it comes to breast cancer diagnosis and treatment. Our findings can contribute to creating a better infrastructure to get all people the same level of care.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age
  • Sex at Birth
  • Gender Identity
  • Sexual Orientation
  • Geography
  • Disability Status
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Registered Tier

Research Team

Owner:

  • Sage Lucas - Graduate Trainee, University of Florida

Breast Cancer Treatment

We to apply machine learning and optimization to the personalization of treatment sequences for patients with metastatic breast cancer. There are several decisions that must be made when designing a treatment regimen for an individual, such as which treatment to…

Scientific Questions Being Studied

We to apply machine learning and optimization to the personalization of treatment sequences for patients with metastatic breast cancer. There are several decisions that must be made when designing a treatment regimen for an individual, such as which treatment to start with, when to change treatments, and how to account for response uncertainty.  We hypothesize that a patient's response trajectory is dependent on not only the regimens used for treatment, but also the sequence of these regimens. By applying machine learning, we can accurately predict how a patient might respond to different regimens based on their clinical features. We can then use this knowledge to select the most promising treatment sequences.

Project Purpose(s)

  • Disease Focused Research (breast cancer)
  • Methods Development

Scientific Approaches

We leverage state-of-the-art machine learning algorithms to gain insight into predictors of treatment response length, fitting separate models for distinct regimens. We create a feature space of patient demographics, disease characteristics, clinical variables, and treatment regimens and define our outcome variable as the duration of the regimen. We will train various algorithms to predict regimen duration and determine the best modeling approach with consideration of both quantitative performance and clinical interpretability. We then couple the regimen-specific machine learning models with mixed-integer optimization to identify the best sequence of treatment regimens for an individual.

Anticipated Findings

The All of Us dataset will allow us to gain a better understanding of treatment response on an individualized level through synthesis of a large, diverse cohort of patients with metastatic breast cancer. We hope to get insights into clinical indicators of treatment success and the interdependence of successive treatment regimens. Ultimately, we hope to propose personalized treatment recommendations that can improve treatment duration and success.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Holly Wiberg - Graduate Trainee, Massachusetts Institute of Technology

Breast Cancer Treatment (Data v4)

We to apply machine learning and optimization to the personalization of treatment sequences for patients with metastatic breast cancer. There are several decisions that must be made when designing a treatment regimen for an individual, such as which treatment to…

Scientific Questions Being Studied

We to apply machine learning and optimization to the personalization of treatment sequences for patients with metastatic breast cancer. There are several decisions that must be made when designing a treatment regimen for an individual, such as which treatment to start with, when to change treatments, and how to account for response uncertainty.  We hypothesize that a patient's response trajectory is dependent on not only the regimens used for treatment, but also the sequence of these regimens. By applying machine learning, we can accurately predict how a patient might respond to different regimens based on their clinical features. We can then use this knowledge to select the most promising treatment sequences.

Project Purpose(s)

  • Disease Focused Research (breast cancer)
  • Methods Development

Scientific Approaches

We leverage state-of-the-art machine learning algorithms to gain insight into predictors of treatment response length, fitting separate models for distinct regimens. We create a feature space of patient demographics, disease characteristics, clinical variables, and treatment regimens and define our outcome variable as the duration of the regimen. We will train various algorithms to predict regimen duration and determine the best modeling approach with consideration of both quantitative performance and clinical interpretability. We then couple the regimen-specific machine learning models with mixed-integer optimization to identify the best sequence of treatment regimens for an individual.

Anticipated Findings

The All of Us dataset will allow us to gain a better understanding of treatment response on an individualized level through synthesis of a large, diverse cohort of patients with metastatic breast cancer. We hope to get insights into clinical indicators of treatment success and the interdependence of successive treatment regimens. Ultimately, we hope to propose personalized treatment recommendations that can improve treatment duration and success.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Holly Wiberg - Graduate Trainee, Massachusetts Institute of Technology
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