Collaborative Analytics Workstream

Mission

Build a secure enclave and collaborative portal for deployment of machine learning and other analytical tools and methods to address key clinical and translational questions. Work collaboratively to generate insights related to COVID-19 from the harmonized limited data set. Experts in artificial intelligence (AI), machine learning (ML), and other technologies assist with review and portal architecture iteration to ensure fit-for-purpose implementation.

 

Register for Meetings
Mondays 2:00 pm PT/5:00 pm ET

Connect with Us:

  1. Onboard to N3C using the link below.
    • In there you will provide your email address. We will add that email address to the CD2H workspace.
  2. Go to our workstream Slack channel directly using the link provided below.
    • Login with your Slack credentials.
Collaborative Analytics Workstream Icon

Leadership and Administration

Joel Saltz Headshot

Stony Brook University

Workstream Lead
Usman Sheikh Headshot
Usman Sheikh

NCATS

Project Manager
Rafael Fuentes Headshot
Rafael Fuentes

NCATS

Project Manager

Subgroups

Facilitate the creation of clinical and methodologic domain teams, and the identification and creation of COVID-19 focused data elements and core analytic workflows.

Peter Robinson (Jackson Laboratory), Heidi Spratt (University of Texas Medical Branch), Tell Bennett (University of Colorado)

Tuesdays 9:00 am PT/12:00 am ET Register Here

Leverage existing efforts across consortia to create comprehensive data extraction for COVID-19 research.

Office Hours: Every other Thursday 12-1pm PT/3-4pm ET  Sign up here.

Hongfang Liu (Mayo Clinic), Hua Xu (UTH), Peter Szolovits (MIT)

Thursdays 12pm PT/3pm ET Register Here

Develop curated data elements around COVID-19 outcomes and progression, and organize data to facilitate analyses involving temporal evolution.

Dave Eichmann (University of Iowa), Warren Kibbe (Duke University)

Wednesdays 11:00 am PT/2:00 pm ET Register Here

Develop and deploy analytic tools and resources driven by clinical scenarios, including analytic tools for clinical data, knowledge graphs, and for clinical data linked to knowledge graphs.

Andrew Williams (Tufts University), Chunlei Wu (Scripps Research Institute)

Fridays 1:00 pm PT/4:00 pm ET Register Here