Research Projects Directory

Research Projects Directory

1,637 active projects

This information was updated 5/28/2022

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.

2 projects have 'Breast Cancer Treatment' in the project title
< Go back to All Projects View or enter a new search query

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

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
1 - 2 of 2
<
>
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.