Combining disparate datasets and making them accessible to data scientists and researchers is a prevalent challenge for many organizations, not just in healthcare research. American Heart Association (AHA) has built a data science platform using Amazon EMR, Amazon Elasticsearch Service, and other AWS services, that corrals multiple datasets and enables advanced research on phenotype and genotype datasets, aimed at curing heart diseases. In this session, we present how AHA built this platform and the key challenges they addressed with the solution. We also provide a demo of the platform, and leave you with suggestions and next steps so you can build similar solutions for your use cases.

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AWS re:Invent
American Heart Association: Finding Cures to Heart
Disease Through the Power of Technology
Dr. Jennifer Hall, Chief—Institute for Precision Cardiovascular Medicine, @jen_precision
Laura Stevens, Research Fellow—American Heart Association
Bob Strahan, Sr. Consultant—AWS Professional Services
ABD316
November 28, 2017

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Agenda
PART 1: The AHA Precision Medicine Platform—why it’s important and what it is/will be used for
Inspire with the purpose, mission, and relevance of the platform. Life is why!
PART 2: The heart of the architecture—harmonize, search, and analyze
Share how PMP got started, key goals, core concepts, challenges, and solutions
PART 3: The PMP today
Live demo of today’s PMP—dataset search, request access, launch workspace, do science
PART 4: What’s Next?
Adopt new AWS services and tools; learn more about deploying and managing PMP; learn how
to deploy the reference solution and experiment for yourself; final takeaways
PART 5 – Questions

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The AHA Precision Medicine Platform
PART 1

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1
OVER 75%
of cardiovascular disease deaths take place
in LOW-AND MIDDLE-INCOME COUNTRIES.
EVERY 2 SECONDS
someone around the world dies
from cardiovascular disease.
Cardiovascular
diseases
are the number
cause of DEATH IN
THE WORLD.
The global cost of
cardiovascular
disease
is approximately
$900
BILLION
and will
exceed
$1TRILLION
BY 2030
The global impact of cardiovascular diseases

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Technology transforming science:
improving health and health-care outcomes
Access Sharing Analytics Improved Patient
Outcomes

6. The promise of personalized medicine

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What problem are we trying to solve?
NEW TECHNOLOGY
AND CAPABILITIES
OLD MODELS
FOR DATA ACCESS AND ANALYTICS

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Modernizing health care in the cloud:
New ways to develop, integrate, and utilize data

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New models for research:
Removing traditional barriers to scientific discovery
Faster
access to data
One marketplace
for datasets
More time/money
spent on research
Improved patient
outcomes

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The heart of the architecture
PART 2

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It started with a conceptual architecture
Amazon S3Amazon S3
upload

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At the heart are three core concepts
Harmonize: Enable search and analysis across datasets
Search: Find the data you care about
Analyze: Prove your hypotheses, create and share insights,
advance science

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Scenario
To prove or refute your hypothesis, you need to find
datasets, combine them, and analyze their data.

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Discordant datasets
Created at different times by different people
Use different names to mean the same thing, and the same
names to mean different things
Use different units of measurement, different scales, different categories
Instruments weren’t calibrated to a common standard
DATA QUALITY ISSUES

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Harmonization

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Harmonization challenges
Sometimes harmonization is easier said than done.
Easier
Harder
Standardize variable names
Standardize units of measurement
Align continuous with categorized values
Align readings from different instruments/calibrations
Align measurements when procedures vary
Align survey responses when questions vary
Information missing from dataset

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Harmonize on AWS
Amazon S3
Python or R
Jupyter Notebooks
Apache Spark
Amazon EMR (compute)
Raw
datasets in
Amazon S3
Amazon EMR
Notebook
plus
harmonized
datasets in
Amazon S3

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Raw datasets
Ingest
1
Harmonize
3
Explore
2
Store
4
Amazon EMR (Spark)
Amazon
S3
HarmonizedRaw
Harmonize

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Load & explore

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Transform Rename
variables
Standardize
‘gender’
categories

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Add variables

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Create dictionary
Variable
name
Value
distribution
stats
Descriptive
metadata

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Save data & dictionary to Amazon S3 & Amazon
Elasticsearch Service
Save subset of
variables to
Amazon ES
Save all variables
to Amazon S3 as
SparkSQL table

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Harmonization notebooks: Save notebook
Notebook saves itself,
along with the harmonized
data it creates

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Search & discovery
• Quickly find relevant data
• Preliminary analysis of filtered data
• Link to harmonization notebook
• Amazon ES
• Index created from Spark
harmonization
• Data (search)
• Metadata (UI filters)
• Filter accordion panel and dashboard
Embedded
dashboard
Links to
harmonization
notebooks

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Search page—dynamic UI filter bar

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Amazon ES
Raw datasets
Amazon EC2
Container
Service (Amazon
ECS)
ALB
Ingest
1
Kibana
Search
filters
NGINX
Harmonize
3
Explore
2
Store
4
Amazon EMR (Spark)
ES
Proxy
ES
Proxy
Amazon S3
HarmonizedRaw
Harmonize Search and discover

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Researcher workspaces
- Python or R
- Jupyter Notebooks
- Spark
- EMR
- Create clear, beautiful, executable,
reproducible science
Same platform as harmonization
From: Notebook Gallery

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Researcher accounts
• Researcher workspaces can run in different (researcher dedicated) AWS
accounts
• Amazon S3 bucket access policies used to grant secure dataset access to
researcher accounts (or users/roles within account)
• Researchers can bring their own datasets into Amazon S3 buckets in their
own accounts
• Amazon EMR/Spark supports wide variety of scalable data science and
genomics tools
• In the PMP, researcher workspaces are created, secured, and managed on
researcher’s behalf

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The PMP today
Demo: precision.heart.org
PART 3

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Demo: precision.heart.org

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What’s next?
PART 4

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Serverless harmonization
- AWS Glue
- Serverless
- Schema inference
- PySpark code
- Extensible
(custom code/libraries)

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Standardize on Parquet file format
All harmonized datasets saved as Parquet files instead of CSV
All harmonized datasets in AWS GLUE catalog
Genomics files, too!
- FASTA, BAM, VCF to Parquet using ADAM
Enables SQL analysis via Amazon Athena and/or Amazon Redshift
Spectrum
• Fast and scalable
• Access to SQL-based BI and data science tools

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Serverless analytics
- Amazon Athena
- Serverless SQL
- Data stays on Amazon S3
- Tables created by Spark
harmonization
- Amazon QuickSight
- Serverless, easy, fast, beautiful,
shareable

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DevSecOps, compliance, etc.
Partner-led PMP session in HCLS track (HLC309)
• Architecture
• Compliance
• Security
• Monitoring (New! Amazon Macie)
• Processes
• DevOps
• Much more

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Harmonize, search, & analyze loosely coupled
datasets on AWS (AWS Big Data Blog)
• Companion sample app—deploy
reference architecture and
samples with one-click launch
button
www.amazon.com/harmonize-search-analyze

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Key lessons learned
• Academic community in cardiovascular and stroke sciences needs
more computer engineers, architects, and bioinformaticists
• Scientific community needs more tools within a cloud
marketplace that do not always require coding
• Scientific community needs data-use cases to help inform them
of the power of cloud computing
• Academic communities are not geared toward a financially simple
model of cloud computing

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Benefits of working with AWS
• Multitude of services that improve the ability to accelerate
scientific discoveries by improving access to data, how data is
searched data, and tools in the workspace to analyze data
• AWS brings experts in cloud architecture, engineering and
software that enable scientists to accelerate their research.
• AWS and AHA are partnering to bring educational tools to the
marketplace for youth, trainees, and professionals.

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The future of precision.heart.org
• Marketplace for scientists, clinicians, patients, consumers, and
communities
• Community forum
• Link to journals
• Educational tools
• Data challenges
• Artificial Intelligence in clinical Medicine, scientific
discoveries, and communities
• Direct-to-participant recruitment—MyResearchLegacy.org

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Questions
PART 5

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Thank you!

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DEMO