Research Projects Directory

Research Projects Directory

10,577 active projects

This information was updated 4/26/2024

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.

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1 project has 'Asthma Attack Prediction: Machine Learning Approach' in the project title
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Asthma Attack Prediction: Machine Learning Approach

Despite progress in therapeutic approaches, according to CDC about 50% of individuals with asthma continue to experience asthma attack (Asthma exacerbation) every year. Asthma attack is an acute episode of progressively worsening of asthma symptoms. Uncontrolled asthma (episodes of asthma…

Scientific Questions Being Studied

Despite progress in therapeutic approaches, according to CDC about 50% of individuals with asthma continue to experience asthma attack (Asthma exacerbation) every year. Asthma attack is an acute episode of progressively worsening of asthma symptoms. Uncontrolled asthma (episodes of asthma exacerbation) can have a significant impact on patients' quality of life, healthcare cost and burden, and time away from school and work. Predicting when exacerbation may occur with adequate intervention would reduce healthcare burden, save cost, and improve quality of life. However, efficient methods that can help identify personalized risk factors and make early predictions are lacking. The objective of this project is to use machine learning models to better predict the risk of asthma exacerbations and calculate saved healthcare cost. All of Us Research database with about 35,000 asthma diagnosed patients and about 10,000 asthma attack events provide an opportunity to build a prediction model.

Project Purpose(s)

  • Disease Focused Research (asthma)

Scientific Approaches

We will retrospectively build a cohort of asthma patients and follow them for asthma exacerbation events. Exacerbation events will be defined as those who needed 1) an oral glucocorticoid prescription for less than 28 days (glucocorticoid burst), 2) an emergency department visit, or 3) hospitalization. Predictive models will be built using a gradient-boosting-machines framework. We will attempt to predict by healthcare utilization type (emergency and hospitalization). Prediction model will be built for pediatric and adult age groups separately. For each outcome, 80% of the dataset serves to train the model and 20% to validate it.

Anticipated Findings

The out come of this project is asthma exacerbation prediction model that is trained and validated on individuals from diverse geography and communities in the US.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Kedir Turi - Early Career Tenure-track Researcher, Indiana University

Collaborators:

  • Sahithi Thummuri - Graduate Trainee, Indiana University
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