Learn how Graph Databases and Graph Data Science can connect your data and provide increased visibility and deep insight into the complex relationships of patient journeys.

1. 1
Delivering Insight into
Complex Patient Journeys
with Graph Analytics

2. 2
• Welcome & Introduction
• Graphs in Life Sciences:
Patient Journey insights
• Patient Journey Case Study /
demonstration
• Q&A
David Rosenblum – Sr Field Engineer
Joe O’Donnell – Account Executive, Life Sciences
Alicia Frame, Data Science Product Manager

3. WE LIVE IN A CONNECTED
SOCIOECONOMIC WORLD

5. Neo4j Innovation Lab
The Wrong Tool for the Job

7. Social Media
Graphs analyze social
relationships
Website Search
Graphs rank web pages in their
search engine results
Product
Recommendations
Graphs understand
behavior and preferences
of online buyers
Knowledge GraphSocial Graph Customer 360 Graph
Property Graphs = Relationships

8. 8
• + 40,000 publications, case statistics, genes
and functions, molecular data and more
• Explore papers, patents, existing treatments
and medications around the family of the
corona viruses and other connected diseases
Neo4j in Healthcare & Life Sciences
Product Brand Management RWE Knowledge Graph
• Connecting 50 research databases, 100k’s of
Excel workbooks, 30 bio-sample databases
• Bytes 4 Diabetes Award for use of a
knowledge graph, graph analytics, and AI
• Customized views for flexible research angles
Drug Discovery
• Competitive evaluation project with 3 yrs of
visits, tests & diagnosis with 10’s of Bn of
records
• Finding similarities in patient journeys
• Graph algorithms for identifying
communities & Key Opinion Leaders

9. Graph
Transactions
Graph
Analytics
Data Integration
Development
& Admin
Analytics
Tooling
Drivers & APIs Discovery & Visualization
Developers
Admins
Applications Business Users
Data Analysts
Data Scientists
Enterprise Data Hub
You need more though…

10. Patient Journey Insights
10

11. Node
● Represents an entity within the graph
● Can have labels
Relationship
● Connects a start node with an end node
● Has one type
Property
● Describes a node/relationship: e.g. name, age, weight etc
● Key-value pair: String key; typed value (string, number, list, ...)
11
Property Graph Model

12. 12
Leveraging Patient Journeys
Algorithms &
Machine Learning
Global analyses to find
trends and predict outcomes
Queries
Fast, local decisioning
and pattern matching
You know what you’re looking for and are
answering questions or extracting data
You’re learning the overall structure of
your graph to make inferences

13. Example questions:
- What do the clinical histories of patients
with a particular disease look like?
- Can we identify off-label drug use or
unexpected clinical outcomes?
Applications:
- Stratify patients into relevant subgroups
- Use graph features to predict outcomes
13
Patient Journey for R&D

14. Example questions:
- Can we identify patterns associated with
specific outcomes and change them?
- Are resources (clinicians, equipment,
diagnostic tests) constrained? Are there
alternatives?
Applications:
- Improving health outcomes
- Cost savings measures
14
Patient Journey for Interventions

15. Example questions:
- What happened to the patients in my
treatment group? Were they different
from the control?
- Can we identify adverse outcomes and
their causes?
Applications:
- Data lineage and transparency
- Causal modeling & advanced ML
15
Patient Journey for Regulatory and
Clinical Trials

16. 16
Neo4j Patient Journey
Demonstration

17. Data loaded into Neo4j
Synthea
- Synthea is an open-source patient
population simulation.
- The Data is fake.
- There are 1MM patients in our graph,
with their encounters, providers,
observations, procedures, conditions
and prescriptions.
- You could have additional
data -- genomic profiles,
expression data, test
results

18. The Model

19. It’s a Database - you can query it

20. Visualizations to Drive Decision Making…

21. …. with drill through capabilities

22. Individual Journey….

23. ….Or “top n” Journies

24. Bloom…. Allergy Community

25. Bloom… with guided paths

26. Node similarities via GDS Louvain &
Community Detection algorithms

27. 27
Neo4j Upcoming Events!

28. For more info/register, visit
neo4j.com/connections
Upcoming Connections
30 June 2020 Graphs in Life Sciences & Healthcare
28 July 2020 Graphs in Risk Mitigation
25 Aug. 2020 Graphs in Telco
17 Nov. 2020 Graphs in Government
A digital event series to keep the
graph database community
connected, energized, and
engaged in education.

29. Other
Digital
Events
Join Us!
17 June 2020 Patient Journey Graph Data Analytics
Workshop
30 June 2020 Graphs in Life Sciences & Healthcare
Event Survey Coming in Email – Your Feedback is Important.
Contact us:
Ø Alicia Frame – Alicia.frame@neo4j.com
Ø David Rosenblum – David.Rosenblum@neo4j.com
Ø Joe O’Donnell – joe.odonnell@neo4j.com