Dave Iberson-Hurst, S-cubed GrapTalk Copenhagen

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Killing Data Silos in the
Life Sciences with Neo4j
Dave Iberson-Hurst
Partner, S-cubed
16th September 2019
Copenhagen
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S-cubed
• A3 Suite platform
• MDR
• SWB
• Linked Data Services
• CDISC Training and
Support
• Regulatory Development
Strategy
• Clinical Trial Documentation
• Marketing Authorisation
Applications & Licence
Maintenance
• EU SME Status, EU
OMPD Holder
• QA & GXP Services
• Statistical Consultancy
• SAS Programming
• Data Management
• CDISC services
• Statistical Analysis and
Reporting
• Quality Assurance
• Biostatistics
• Clinical Data
Management
• Pharmacovigilance
•Medical Monitoring
• Risk Based Monitoring
• Operational Reporting
• Qlik Extensions
Data Analytics
(Qlik) Biometrics
Clinical
Standards
Management
Regulatory
Affairs

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Silos
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Silos … but connected

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Why Clinical Research suffers from silos:
o Length of clinical programmes
o Number of clinical studies in a programme
o Diversity of systems
o Organizational Silos
o A world of Excel and PDFs
o Outsourcing
o Complexity
o We build in one silo, send it onto the next
o We end up with copies of the data for each
different purpose
o Standards
Clinical Research and Silos
Protocol
Programme
Management
Data Capture
System(s)
Standards
Regulatory
& Safety
System(s)
Data
Warehouse
Organisational
Boundary
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o Multiple Standards
o Different types of standards
• Data Exchange (XML)
• Content
• Terminology
o Developed since 2000
o Developed in Silos
o Traditionally delivered as PDF
o Moving to electronic
• MS Excel
• XML
• RDF
o Submission for regulatory approval
of products requires use of the
standards
Data Standards
Play a big role in our work

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Our “Study” World
Collect Organize Analyse ResultsPlan
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Our Silos
o This world is just too big to
understand, too big to fit into our
heads
o So, as humans, we did what we
always do, we cut it up, we
decomposed the problem
o We developed our standards in
silos
o As a consequence our data ends
up in silos

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Our Silos
o And where we cut we lost the
relationship
o And so later we use programmers
to replace those relationships, stitch
the data back together
o And each programmer / company
potentially does it differently
o This causes issues, e.g. data
becomes difficult to pool
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Neo4j
Property Graph holding multiple study definitions.
Links back to triple store definitions
Apache Jena/Fuseki
Triple Store holding core definitions
(e.g. standards)
PostgreSQL
Relational DB holding users, audit trail etc.
Architecture

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Architecture
Baseline Definitions
Study Definitions
imports
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Benefits
o Neo4j holds our study definitions
o Use a graph model that replaces the
“lost” relationships
o Based on solid foundation of industry
definitions sourced from the RDF world
o Allows for
• Scale
• Use of third party tools
• Take advantage of the large user base
• Cross industry use and ideas
o The graph allows us to iterate
development without significant impact
on previous work

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Benefits
o The graph provides for traceability that
is required for regulatory submission
o Provides for a single source of data
o Our required standards become
outputs from graph queries reducing
the “copies” of data
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Neo4j Use

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Study Workbench
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Study Workbench

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Electronic Health Records
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Mining for Definitions

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And Some Cypher …
o We use a lot of rectangular
structures, but we can recreate these
with Cypher queries
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So …
+ =

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Contact Details
Dave Iberson-Hurst
dih@s-cubed.dk