Mark Weiner

Mid-career Tenured Researcher, Cornell University

3 active projects

Practice Notebook to Explore AoU dataset

This project will explore the scope of patients with COVID-19 and the characteristics of patients with PASC.

Scientific Questions Being Studied

This project will explore the scope of patients with COVID-19 and the characteristics of patients with PASC.

Project Purpose(s)

  • Educational
  • Other Purpose (practice notebook to familiarize with RW)

Scientific Approaches

We will apply algorithms developed by the RECOVER PCORnet Adult Cohort and compare the overlap in cohorts with the set derived though the N3C algorithm

Anticipated Findings

We expect to find a high degree of concordance between the RECOVER Adult Cohort algorithm and the N3C algorithm, even though the approaches were developed through different machine learning methods on different source patient data sets

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • 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

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

Duplicate of Duplicate of Phenotype - Ischemic Heart Disease (v6)

The Notebooks in this workspace can be used to implement well-known phenotype algorithms in one’s own research.

Scientific Questions Being Studied

The Notebooks in this workspace can be used to implement well-known phenotype algorithms in one’s own research.

Project Purpose(s)

  • Educational
  • Methods Development
  • Other Purpose (This is an All of Us Phenotype Library Workspace created by the Researcher Workbench Support team. It is meant to demonstrate the implementation of key phenotype algorithms within the All of Us Research Program cohort.)

Scientific Approaches

Not Applicable

Anticipated Findings

By reading and running the Notebooks in this Phenotype Library Workspace, researchers can implement the following phenotype algorithms:

Christianne L. Roumie; Jana Shirey-Rice, Sunil Kripalani. Vanderbilt University. MidSouth CDRN - Coronary Heart Disease Algorithm. PheKB; 2014. Available from https://phekb.org/phenotype/234

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Mark Weiner - Mid-career Tenured Researcher, Cornell University
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