Graphs and Artificial Intelligence have long been a focus for Franz Inc. and currently we are collaborating with Montefiore Health System, Intel, Cloudera, and Cisco to improve a patient’s ability to understand the probabilities of their future health status. By combining artificial intelligence, semantic technologies, big data, graph databases and dynamic visualizations we are deploying a Cognitive Probability Graph concept as a means to help predict future medical events. The power of Cognitive Probability Graphs stems from the capability to combine the probability space (statistical patient data) with a knowledge base of comprehensive medical codes and a unified terminology system. Cognitive Probability Graphs are remarkable not just because of the possibilities they engender, but also because of their practicality. The confluence of machine learning, semantics, visual querying, graph databases, and big data not only displays links between objects, but also quantifies the probability of their occurrence. We believe this approach will be transformative for the healthcare field and we see numerous possibilities that exist across business verticals. During the presentation we will describe the Cognitive Probability Graph concepts using a distributed graph database on top of Hadoop along with the query language SPARQL to extract feature vectors out of the data, applying R and SPARK ML, and then returning the results for further graph processing. #AllegroGraph

1. The power of the
Cognitive Probability Graph
(aka Cognitive Computing)
June 2016
Jans Aasman
ja@franz.com

2. 10 years ago
Structured Data

3. 7 years ago
Structured Data Unstructured Data

4. 4 to 5 years ago
Structured Data
Unstructured
Data
Knowledge
Domain knowledge
Linked Open Data
Vocabularies
Taxonomies/Ontologies

5. New #1: Learning. Feed output of data
science back into data infrastructure
Structured
Data
Unstructured
Data
Knowledge
Domain knowledge
Linked Open Data
Vocabularies
Taxonomies/Ontolo
gies
Probabilistic
Inferences.

6. New # 2: everything in one (distributed)
semantic graph
Structured
Data
Unstructured
Data
Knowledge
Domain
knowledge
Linked Open Data
Vocabularies
Taxonomies/Ontol
ogies
Probabilistic
Inferences.
Unstructured
Data
Knowledge
Domain
knowledge
Linked Open Data
Vocabularies
Taxonomies/Ontol
ogies

7. AKA: Cognitive Computing
Structured
Data
Unstructured
Data
Knowledge
Domain
knowledge
Linked Open Data
Vocabularies
Taxonomies/Ontol
ogies
Probabilistic
Inferences.
Unstructured
Data
Knowledge
Domain
knowledge
Linked Open Data
Vocabularies
Taxonomies/Ontol
ogies

8. Examples
Examples
• Healthcare: If I have this class of diagnostics and I get this procedure what are some of the new
symptoms I might get in the next two years.
• eCommerce and brand protection: find all my products based on product similarity
• Logistics: what can I statistically predict about part P breaking down and what other parts do I
usually buy after that part breaks down.
• Police Intelligence: find the most plausible story of a temporally orderend shortest path between
two criminal through observed (hard) facts and inferred (soft facts)
• Fraud detection: find links between your local chamber of commerce and the panama papers
through similar names and addresses.

9. Example healthcare
• Franz and Montefiore are partners in the Semantic Data Lake project.

10. One cognitive computing platform for all healthcare analytics

11. Example healthcare
• We created a single data centric platform that can serve any type of
analytic without building a new data mart for every new question.
• Currently 2.7 million patients with 10 years of data
• All data captured in a Unified Clinical Event model with 350 classes of
events.

12. Healthcare: structured and unstructured data

14. Structured patient data combined with complex
integrated terminology

15. Provenance for every value

16. Healthcare: the knowledge bases
• More > 180 vocabularies and terminology systems integrated in on
unified terminology system (Mesh, Snomed, UMLS, RxNorm, LOINC etc,
etc)
• External databases and
• Linked Open Data

17. OMOP
11089001
6600349
11894800
5
7534205
16790501
14667809
35896705
9209005
1732609
9908905
1469609
329005
LOINC
113345001 140460009
skos:semanticRelation
skos:narrower
118948005
skos:broader
“9209005”
SNOMEDCT
M0024135
M0008124 M0004742
skos:semanticRelation
skos:narrower
M0015742
skos:broader“Abdominal Pain”
“M0024135”
skos:exactMatch
A0549302
A0978543
skos-xl:prefLabel
9209005
“Abdominal Pain”
SAB
AUI
SUI
MeSH
SNOMEDCT
MedDRA
rdfs:subClassOf
rdf:type
C0172359 C0232487
C0238551
“Abdominal Pain”
“C0000737”
skos:semanticRelation
skos:broader
UMLS - MTH
skos:notation
S035799
skos-xl:label
MTH
STR
C000737
Everything linked through SKOS
SKOS/SKOS-XL
ConceptScheme
Concept
UMLS - Semantic Net
Entity Event
Label

18. Population,
Community
Time
Pt.Pt.Pt.Pt.
SDL Paradigm:
Pt.Pt.Pt.Pt.
Diagnosis
Codes
Disease
Classification
OMIM,
GONG
Genetic
Profile
Procedure
CodeHCPC
Manufacturers
PharmKGB
Drug
Classification
Drug
Codes
DrugBank
ClinicalTrials
CER
PubMed
Analytic Tapestry
(closed loop analytics)

21. Healthcare: probabilistic inferences
Why is this so important?
• Usually the output of data science results in reports and publications but
• No formal trace where the data came from
• No formal link to the actual methods you used, or who did it, or when you did it
• Cannot be compared to earlier results
• Cannot be used as building blocks for further research
• In general : the output is not queryable
• This is not good for delivery of care, reproducibility of research findings,
security and compliance, and results in loss of value-added information,
and enterprise intellectual property and assets, and unnecessary
duplication of efforts

22. Odds ratio

26. Association rules

28. K-means clustering

31. And then a query you could do never before
• Using the Knowledge Base, the Structured Data and the Probabilistic
inferences all at the same time.
• To find the statistical links between Diabetes and Vision problems in our
Semantic Data Lake
• Find the set of ICD9s that are connected via one or more steps to
concepts in the KB that mention Diabetes
• Find the set of ICD9s that are connected via one or more steps to
vision* or eye* or retinal*
• An show how those two sets are related in the space of odds ratios

34. And then just a few other examples

35. In the ecommerce world: find similar objects based on > 10
criteria, including description, product codes, pictures, etc

37. Returns a Graph in a Table (ughh )

38. But powerful when visualized

39. Or like this

40. And linking with the panama papers

41. And now the researchers can start investigating

42. Summary: this is the new paradigm of computing
Structured
Data
Unstructured
Data
Knowledge
Domain
knowledge
Linked Open Data
Vocabularies
Taxonomies/Ontol
ogies
Probabilistic
Inferences.
Unstructured
Data
Knowledge
Domain
knowledge
Linked Open Data
Vocabularies
Taxonomies/Ontol
ogies