Kathy McKeown, PI Coordinated by Columbia University NE Regional - - PowerPoint PPT Presentation

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Kathy McKeown, PI Coordinated by Columbia University NE Regional - - PowerPoint PPT Presentation

Nov 14, 2022 97 likes •433 views

Ren Bastn, Executive Director Kathy McKeown, PI Coordinated by Columbia University NE Regional Big Data Innovation Hub Provide frameworks for public-private, multi-sector collaborations to address high-priority challenges with data-driven

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René Bastón, Executive Director Kathy McKeown, PI

Coordinated by Columbia University

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NE Regional Big Data Innovation Hub

Columbia (PI)

Provide frameworks for public-private, multi-sector collaborations to address high-priority challenges with data-driven solutions

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By the Numbers

Community/Communic ications:

  • Events: Convened or participated in

28 events; over 6000 people

  • Website Relaunch: Blog, Newsletter,

Twitter; ~9000 visitors/month Altogether, 10s thousands reached

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By the Numbers

Add’l Fun Fundin ing and and In In-Kin ind Con

  • ntr

trib ibutio ions

  • $4.9 million

10 New Proje

  • jects
  • 3 Spoke Projects
  • 4 Planning Projects
  • Innovator Internships
  • BD-Map - CRUX
  • Cybersecurity Risk
  • NTIS Joint Venture Partnership
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Cross-Sector Outreach

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Cross-Sector Outreach

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Cross-Sector Outreach

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International Educational Data Mining Society

Cross-Sector Outreach

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A Licensing Model and Ecosystem for Data Sharing

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What’s Next?

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Next Steps & New Directions

  • Expanded Access to Data
  • BD-Map Pilot Funded; initiative

launch Q1 2018

  • Collaboration/Consortia Models
  • Cybersecurity Risk – Seed funded;

initiative launch Q1 2018

  • Transportation – partnerships in the

works

  • NTIS JVP
  • Non-profit
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For More Information

nebigdatahub.org rb70@columbia.edu contact@nebigdatahub.org @NEBigDataHub #BDHubs | #NEBigData tinyurl.com/NEBDHubList

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Chirag Patel Noemie Elhadad, Vasant Honavar, Greg Cooper

chirag@hms.harvard.edu @chiragjp www.chiragjpgroup.org

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Integration of E and causal reasoning approaches for large-scale observational health research: key investigators

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higher pollution lower pollution

Increase translational impact of this type of research through: scale and accessibility! (the exposome, phenome, larger and more generalizable populations)

15% increased risk for death

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Many hypotheses that we need to address to understand relationship between disease risk and environment!

What is the effect of air pollution levels in disease? Do adverse weather conditions influence hospital use? What pharmaceutical drugs lead to adverse health outcomes? How does socioeconomic context influence hospital use, disease rates, and recovery?

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Integrating the ExposomeDB with OHDSI and causal modeling tools to drive and demonstrate discovery.

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Capitalize on digitalized health record data (from around the world)! High-powered dataset(s) for discovery

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… and where do we get environmental information?

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Examples of sources of disparate environmental datasets available in the Exposome Data Warehouse

Geological NASA - Cloud and Atmosphere Profiles NOAA Climate Data Pollution EPA Air Quality Surveillance Data Mart, or AirData, Soc io-Economic US Census American Community Survey (ACS) Epidemiological CDC Wonder, USDA Food Atlas Chirag Lakhani

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Mashing up Exposome Data Warehouse with patient data from OHDSI

f(location, time)

PM2.5 income Pollen count

EPA AirData American Community Survey NOAA Climate home zipcode encounter time

Chirag Lakhani

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Lakhani et al, in preparation

ExposomeDB is ready to deploy! Team is writing a manuscript that describes the resource

Re-usable Jupyter notebooks coming soon!

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Causal discovery tools ready to go: ccd.pitt.edu

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http://chiragjpgroup.org/exposome-analytics-course

Nam Pho

Please contact Chirag Patel for help or project ideas!

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Examples of science enabled by these resources:

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“What is the average PM2.5 in May 2016?”

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What about drugs? Possible to repurpose existing drugs to alleviate disease risk?

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Is bupropion associated with better glucose profiles before and after exposure to the drug?

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Is bupropion associated with better glucose profiles before and after exposure to the drug?

yes!

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What’s next?

  • Execute more science!
  • Systematic ExposomeDB and causal inference tools integration with OHDSI
  • Disseminate ExposomeDB resources (Jupyter notebooks and server)
  • Host students!
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Check http://nebigdatahub.org for project updates!

Workshop/Hackathon (in New York or Boston, 2018) Cross-institution short internships Tutorials + Code + Data! Contact us for code and ExposomeDB data! chirag@hms.harvard.edu