rené bastón, executive director kathy mckeown, piychen/dash/slides/rene.pdfne regional big data...
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René Bastón, Executive Director
Kathy McKeown, PI
Coordinated by Columbia Univers i ty
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
By the Numbers
Community/Communications: Events: Convened or participated in
28 events; over 6000 people
Website Relaunch: Blog, Newsletter, Twitter; ~9000 visitors/month
Altogether, 10s thousands reached
By the Numbers
Add’l Funding and In-Kind Contributions $4.9 million
10 New Projects 3 Spoke Projects 4 Planning Projects Innovator Internships BD-Map - CRUX Cybersecurity Risk NTIS Joint Venture Partnership
Cross-Sector Outreach
Cross-Sector Outreach
Cross-Sector Outreach
International Educational Data Mining Society
Cross-Sector Outreach
A Licensing Model and Ecosystem for Data Sharing
What’s Next?
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
For More Information
nebigdatahub.org
@NEBigDataHub
#BDHubs | #NEBigData
tinyurl.com/NEBDHubList
Chirag Patel Noemie Elhadad, Vasant Honavar, Greg Cooper
@chiragjp
www.chiragjpgroup.org
Integration of E and causal reasoning approaches
for large-scale observational health research: key investigators
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
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?
Integrating the ExposomeDB with OHDSI and causal modeling tools to drive and demonstrate discovery.
Capitalize on digitalized health record data (from around the world)!
High-powered dataset(s) for discovery
… and where do we get environmental information?
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
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
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!
http://chiragjpgroup.org/exposome-analytics-course
Nam Pho
Please contact Chirag Patel for help or project ideas!
Examples of science enabled by these resources:
“What is the average PM2.5 in May 2016?”
What about drugs? Possible to repurpose existing drugs to alleviate disease risk?
Is bupropion associated with better glucose
profiles before and after exposure to the drug?
Is bupropion associated with better glucose
profiles before and after exposure to the drug?
yes!
What’s next?
• Execute more science!
• Systematic ExposomeDB and causal inference tools integration with OHDSI
• Disseminate ExposomeDB resources (Jupyter notebooks and server)
• Host students!
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!