Gabriel Anaya

Administrator, National Heart, Lung, and Blood Institute (NIH - NHLBI)

4 active projects

AOU_Recover_Long_Covid_v6

Identify potential long-COVID patients with high accuracy, achieving areas under the receiver operator characteristic curve using the National COVID Cohort Collaborative’s (N3C) EHR repository, we developed XGBoost machine learning (ML) models.

Scientific Questions Being Studied

Identify potential long-COVID patients with high accuracy, achieving areas under the receiver operator characteristic curve using the National COVID Cohort Collaborative’s (N3C) EHR repository, we developed XGBoost machine learning (ML) models.

Project Purpose(s)

  • Disease Focused Research (Long COVID)

Scientific Approaches

Identify potential long-COVID patients with high accuracy, achieving areas under the receiver operator characteristic curve using the National COVID Cohort Collaborative’s (N3C) EHR repository, we developed XGBoost machine learning (ML) models.

Anticipated Findings

Identify potential long-COVID patients with high accuracy, achieving areas under the receiver operator characteristic curve using the National COVID Cohort Collaborative’s (N3C) EHR repository, we developed XGBoost machine learning (ML) models.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • WeiQi Wei - Other, All of Us Program Operational Use
  • Vern Kerchberger - Early Career Tenure-track Researcher, Vanderbilt University Medical Center
  • Srushti Gangireddy - Project Personnel, Vanderbilt University Medical Center
  • Mark Weiner - Mid-career Tenured Researcher, Cornell University
  • Hiral Master - Project Personnel, All of Us Program Operational Use
  • Gabriel Anaya - Administrator, National Heart, Lung, and Blood Institute (NIH - NHLBI)

Collaborators:

  • Chris Lunt - Other, All of Us Program Operational Use

Implementing Recover Algorithm on AOU Data

Identify potential long COVID patients among three groups in the database: All COVID-19 patients, patients hospitalized with COVID-19, and patients who had COVID-19 but were not hospitalized. The models proved to be accurate, as people identified as at risk for…

Scientific Questions Being Studied

Identify potential long COVID patients among three groups in the database: All COVID-19 patients, patients hospitalized with COVID-19, and patients who had COVID-19 but were not hospitalized. The models proved to be accurate, as people identified as at risk for long COVID were similar to patients seen at long COVID clinics.

Project Purpose(s)

  • Disease Focused Research (Long COVID)

Scientific Approaches

XGBoost machine learning model is developed to identify potential patients with long COVID.
Base population is defined as any non-deceased adult patient (age ≥18 years) with either an International Classification of Diseases-10-Clinical Modification COVID-19 diagnosis code (U07.1) from an inpatient or emergency visit, or a positive SARS-CoV-2 PCR or antigen test, and for whom at least 90 days have passed since COVID-19 index date.
The model examines demographics, health-care utilization, diagnoses, and medications for adults with COVID-19.

Anticipated Findings

Identify with high accuracy, patients who potentially have long COVID. Find the important features.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • WeiQi Wei - Other, All of Us Program Operational Use
  • Vern Kerchberger - Early Career Tenure-track Researcher, Vanderbilt University Medical Center
  • Srushti Gangireddy - Project Personnel, Vanderbilt University Medical Center
  • Hiral Master - Project Personnel, All of Us Program Operational Use
  • Gabriel Anaya - Administrator, National Heart, Lung, and Blood Institute (NIH - NHLBI)

Collaborators:

  • Chris Lunt - Other, All of Us Program Operational Use

RECOVER+AoU

The goal of this initial cross-platform testing effort is focused on expanding the analytical capability of available data sources that have collected data on SARS-CoV-2. As we gather data across the US, we can use independent data sources to better…

Scientific Questions Being Studied

The goal of this initial cross-platform testing effort is focused on expanding the analytical capability of available data sources that have collected data on SARS-CoV-2. As we gather data across the US, we can use independent data sources to better understand PASC in our population and identify possible interventions. As a first step, we hope to leverage available RECOVER data tools and apply within the All of Us Researcher Workbench to assess cross-platform interoperability and analytical equivalence. This would provide a path to engage our research community and guide research towards our understanding of PASC.

Project Purpose(s)

  • Population Health
  • Methods Development
  • Control Set
  • Other Purpose (Testing PASC ML Algorithm from N3C-RECOVER in AoU Platform)

Scientific Approaches

Bring existing data query code and data analytics code from the RECOVER researcher team into the All of Us Researcher Workbench. Use “equivalent” code sets to explore and expand our understanding of PASC and its effects on the US population. Share reproducible findings through programming “notebook” and analysis of standardized datasets (OMOP).

Anticipated Findings

This research activity will be developed in conjunction with an awareness campaign of the collaborative efforts undertaken by both RECOVER and AoU. We intend to highlight the available datasets with SARS-CoV-2 data, as well as the cloud-based researcher workspaces (RECOVER, AoU). With the awareness campaign and cross-platform testing, we intent to create an on-ramp for experienced and young researchers within two large and diverse datasets.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • WeiQi Wei - Other, All of Us Program Operational Use
  • Vern Kerchberger - Early Career Tenure-track Researcher, Vanderbilt University Medical Center
  • Srushti Gangireddy - Project Personnel, Vanderbilt University Medical Center
  • Hiral Master - Project Personnel, All of Us Program Operational Use
  • Gabriel Anaya - Administrator, National Heart, Lung, and Blood Institute (NIH - NHLBI)
  • Chris Lunt - Other, All of Us Program Operational Use

epi_dcvs_test041822

This workspace is intended for testing of All of Us platform as part of an effort from NIH and NHLBI to integrate different data sources across institutes. This workspace will be used primarily for testing and education of All of…

Scientific Questions Being Studied

This workspace is intended for testing of All of Us platform as part of an effort from NIH and NHLBI to integrate different data sources across institutes.
This workspace will be used primarily for testing and education of All of Us data tools with no intentions of publication.

Project Purpose(s)

  • Methods Development

Scientific Approaches

The goal is to explore the different datasets available, to have working knowledge of the All of Us platform and for its better application of data at the institute.
Intend to explore dataset variables as used with OMOP and possible application to other datasets.

Anticipated Findings

I do not have any anticipated findings as this is more exploratory to better understand the workflow and data tools used in the Researcher Platform.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Gabriel Anaya - Administrator, National Heart, Lung, and Blood Institute (NIH - NHLBI)
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