Chen Zhang
Early Career Tenure-track Researcher, University of Rochester
2 active projects
Depression and substance among Black women with HIV
Scientific Questions Being Studied
What is the prevalence of depression among Black women who living with HIV?
What factors at multiple levels impact Black women's depression status?
Project Purpose(s)
- Educational
- Other Purpose (This is the first project that I am using to practice AoU data. )
Scientific Approaches
I would use descriptive analysis to identify depression among Black women living with HIV. Subgroup analyses will be employed. Furthermore, multiple regression models will be employed.
Anticipated Findings
Identify prevalence of depression among Black women who live with HIV.
Explore factors that may associated with depression among Black women living with HIV.
Demographic Categories of Interest
- Race / Ethnicity
- Age
- Sex at Birth
- Gender Identity
- Sexual Orientation
- Geography
- Access to Care
- Education Level
- Income Level
Data Set Used
Controlled Tier
Duplicate of AOU_Recover_Long_Covid_v6
Scientific Questions Being Studied
The purpose of this workspace was to implement the published XGBoost machine learning (ML) model, which was developed using the National COVID Cohort Collaborative’s (N3C) EHR repository to identify potential patients with PASC/Long COVID in All of Us Research Program. N3C, All of Us, PCORnet and RECOVER teams collaborated to execute this purpose to enhance the overall PASC/Long COVID efforts.
Project Purpose(s)
- Disease Focused Research (Long COVID)
Scientific Approaches
To achieve this objective, data science workflows were used to apply ML algorithms on the Researcher Workbench. This effort allowed an expansion in the number of participants used to evaluate the ML models used to identify risk of PASC/Long COVID and also serve to validate the efforts of one team and providing insight to other teams. These models were implemented within the All of Us Controlled Tier data (C2022Q2R2), which was last refreshed on June 22, 2022. We intend to provide a step-by-step guide for the implementation of N3C's ML Model for identification of PASC/Long COVID Phenotype in the All of Us dataset. It also evaluated demographic characteristics for participants who were identified as possibly having PASC/Long COVID, and provides additional details on model performance, such as areas under the receiver operator characteristic curve and confusion matrix.
Anticipated Findings
We intend to provide a step-by-step guide for the implementation of N3C's ML Model for identification of PASC/Long COVID Phenotype in the All of Us dataset. The findings and code use to generate the demographic characteristics for participants who were identified as possibly having PASC/Long COVID, and provides additional details on model performance, such as areas under the receiver operator characteristic curve and confusion matrix.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Controlled TierRequest a Review of this Research Project
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