Presented at #H2OWorld 2017 in Mountain View, CA. Enjoy the video: Learn more about H2O.ai: https://www.h2o.ai/. Follow @h2oai: https://twitter.com/h2oai. - - - Data sharing is fraught with privacy concerns in the biomedical domain. How do we develop insights if data silos are our reality? Stanford is undertaking a “data commons on steroids” approach with a goal to “free the scientist” and make insight sharing possible across the data silos. Somalee is a computational physicist by training, a biotechnologist by profession and a data analyst by the way of passion. She believes that with the explosion of data in healthcare and with new methods to analyze such large amounts of data, we will see massive changes in how human diseases are addressed via novel drugs, large-scale genomics, wearable sensors, and software to tie it all together. She wants to drive part of this revolution.

1. Somalee Datta, PhD
Director Research IT,
SoM Information Resources & Technology
Bringing scientists to data
(to accelerate discoveries and improve human health)

2. Can we enable exploration on bigger data,
faster analysis and cheaper hypothesis
testing?

3. System engineering approach to “bigger/faster/cheaper”
Findable, Accessible,
Interoperable, and
Reusable data
Secure workspaces
to interact with data
Research ethics,
and compliance
Privacy protecting data
sharing across silos

4. Ethics and Compliance
&
Privacy protecting data sharing

5. Ethics & Compliance “But even one lapse is one
too many.” - Donna Shalala,
Secretary of the U.S.
Department of Health and
Human Services, NEJM, 2000
In Sep 1999, teenager
Jesse Gelsinger died in
a gene therapy trial at
UPenn.

6. Why is privacy a hard
problem?
P. GOLLE: Proceedings of the 5th
ACM Workshop
on Privacy in the Electronic Society. 2006: 77-80.
5 digit
zip code
County
YYYY 0.2 % 0.0%
MM / YYYY 4.2 % 0.2%
MM / DD / YYYY 63.3 % 14.8%
Probability of re-ID w/ gender known:
More @ http://med.stanford.edu/gapp.html
Security & Privacy
Workshop 2017
Track 1: Develop privacy
preserving patient linkage
technique
Track 2: Develop scalable solutions
using SGX secure hardware to
enable genome variants search
Track 3: Develop Homomorphic
Encryption based ML techniques
over encrypted data

7. Biomedical data analysis
brings novel benefits to
human health

8. Predictive Analytics with 10-20 years of EHR data
More than 60% of deaths
in the US happen in an
acute care hospital. This
predictive model helps the
Palliative Care team to be
engaged early enough to
ensure meaningful
services.

9. Genome based
diagnosis coming to you
● Combines research excellence
with operational delivery
● Public-Private partnership
● Pilot launched in Jan 2014
● Production in Jan 2018
● Genome and exome sequencing
● Three major disease areas:
○ Pediatric and adult syndromes
○ Heritable cancer predisposition
○ Heritable cardiovascular disease
Built on a platform that allows rapid
turnaround from experimentation to
productization

10. Future is now!
More @ http://stan.md/2AldJLE
AFib, the leading
cause of stroke, is
responsible for
approximately 130,000
deaths and 750,000
hospitalizations in the
US every year.
More @ http://stan.md/2iw97f0
Michael Snyder
wearing multiple
devices used in
the study

11. Imaging data - Radiology
(Algorithm + Radiologist) is better than (Radiologist)
Stanford researchers develop convolutional neural network algorithm
that can diagnose up to 14 types of medical conditions based on
chest x-ray image.

12. Potential to make planet scale impact
400 million diabetic patients in world;
70 million in India; 30% estimated to
develop DR
“Piloting this AI-based diagnosis is
Aravind Eye Hospital, the largest eye
care provider in India. Aravind Eye
Hospital contributed to Google’s
research by providing images of DR
patients.” - Forbes, Sep 5, 2017

13. Standing upon shoulders of giants ...

14. NIH BD2K “Data Commons”
Multi-Cloud Data Biosphere Vision @ http://bit.ly/2zNdBBL
● 12 awards form nucleus of
an NIH Data Commons Pilot
Phase Consortium
● $9 Million in 2017
● Using public clouds

15. Sage Bionetworks approach to Open Research

16. Stanford approach

17. Findable, Accessible, Interoperable, Reusable
Vendor Neutral
“DICOM”
Archive
Epic
“Clarity”
data
Genome
Archive
Real Time
Location
System Data
Pathology
Scanner
Data
Archive
Mobile Health
Data
AI
Statistics
Visualization
Data mining

18. Secure workspaces to interact with data
Stanford Data Center at SLAC

19. Research Privacy, Ethics and Compliance
More @ https://redivis.com/StanfordPHS
For a single dataset:
● Data Use
Agreement
● Access “life-cycle”
management
● Compliance
requirements e.g.
IRB approval,
HIPAA training
● Track use
For a single user:
● Single pane of view
to data access
status
● Data flyby -
metadata, counts,
quality checks,
versions
● Access to data
experts
● Access to research
community

20. Let’s build together!