Metadata in clinical healthcare practice. Start making sense of your data and make it F.A.I.R

1. Start Making Sense
Be F.A.I.R
Nordic HealthData – how do we utilize metadata in health practice?

2. INSTITUTIONEN FÖR TILLÄMPAD INFORMATIONSTEKNOLOGI | www.ait.gu.seINSTITUTIONEN FÖR TILLÄMPAD INFORMATIONSTEKNOLOGI | www.ait.gu.se
Moore humor anno 1965
Reality today 2020
http://orangecone.com/archives/2010/04/smart_things_ch_2.html

3. INSTITUTIONEN FÖR TILLÄMPAD INFORMATIONSTEKNOLOGI | www.ait.gu.seINSTITUTIONEN FÖR TILLÄMPAD INFORMATIONSTEKNOLOGI | www.ait.gu.se
It was 20+ years ago today…
Dr Pål Lindström
Pål, founded: LocusMedicus Int.
• A Healthcare Community of Practice
• Knowledge Networking automated and
augmented by AI

4. Challenges

5. Landfill

8. DATA

9. FAIR

10. Organising

11. Organising

12. Decipher

13. Lingua Franca
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= &
'()
$
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𝑥'
𝑎$,'
𝑓 𝑥 = 𝑎) + &
$(.
/
𝑎$ cos
𝑛𝜋𝑥
𝐿
+ 𝑏$ sin
𝑛𝜋𝑥
𝐿
𝑎8
+ 𝑏8
= 𝑐8
1 + 𝑥 $
= 1 +
𝑛𝑥
1!
+
𝑛 𝑛 − 1 𝑥8
2!
+ ⋯
𝑥 =
−𝑏 ± 𝑏8 − 4𝑎𝑐
2𝑎
sin 𝛼 ± sin 𝛽 = 2 sin
1
2
𝛼 ± 𝛽 cos
1
2
𝛼 ∓ 𝛽
𝐸 = 𝑀𝐶8

14. Alphabet Soup
Vocabularies
http://flic.kr/p/zCyMp
Standards

15. Organising Principles
http://flic.kr/p/kRqh42
SKOS
DC
W3C
schema.org
Linked Data
RDF
HL7/FHIR
ISA2
MeSH UMLS SnoMed CT IDC11

16. INFORMATION ARCHITECTURE AS AN ORGANIZING
DISCIPLINE
the discipline of organising
1. What Is Being Organised?
2. Why Is It Being Organised?
3. How Much Is It Being Organised?
4. When Is It Being Organised?
5. Who (or What) is Organising It?
6. Where is it Organised?
16
Robert Glushko

17. INFORMATION ARCHITECTURE AS AN
ORGANIZING DISCIPLINE
● Libraries, markets,
museums, zoos,
vineyards
● Different types of
data and documents
● Personal
information and
artifacts
● People
We
Organise
17
Robert Glushko

18. INFORMATION ARCHITECTURE AS AN ORGANIZING
DISCIPLINE
An “Organising System”
Robert Glushko 18

19. INFORMATION ARCHITECTURE AS AN ORGANIZING
DISCIPLINE
Organising
Books By
Content
Photo by Jeffrey Beall (http://www.flickr.com/photos/denverjeffrey/304220561) Creative Commons CC BY-ND 2.0
Robert Glushko 19

20. INFORMATION ARCHITECTURE AS AN ORGANIZING
DISCIPLINE
Recall:
The “Organising System”
A collection of resources intentionally arranged
to enable some set of interactions
Robert Glushko 20

21. INFORMATION ARCHITECTURE AS AN ORGANIZING
DISCIPLINE
“Information Architecture is designing an
abstract and effective organisation
of information
and then
exposing that organisation to facilitate
navigation and information use”
Intentionalarrangement
Interactionsupport
Robert Glushko 21
Defining “Information Architecture” as
an Organising Discipline

22. Standing on the
shoulders of Giants
No AI without IA
Newton ShannonBayes TuringAl Kindi Ada Wolfram

23. INFORMATION ARCHITECTURE AS AN ORGANIZING
DISCIPLINE
The Document Type Spectrum – A
Continuum Between
Documents and Data
Robert Glushko 23

24. Tools for a smart platform

25. Data Findability:
How Knowledge Graphs can
support FAIR data

26. Google works for us as
consumers, yes?
Weekend search

27. Frustrated by search at work?
Handy-patient summary?
Latest research?
Applicable guidelines?

28. Is your data designed to be found/understood?
Metadata:
what is it? what’s inside it?
what are the ingredients?
Context:
how old is it?
when and where was it made?
what is needed to access the contents?
Structure:
does it need to be cooked/processed?
how long does it need to be cooked for?
Data connections:
will it be like anything I’ve had before?
will it go with the other items I have?
will it be something others like?
Data meaning / comprehension (aka Findability)

29. Google & its Knowledge Graph

30. Bridging the gap: Data meaning v User intent
Situation for most organisations
Google
Google’s Knowledge Graph 2010
(based on Semantic Web-based technologies, incl. RDF)

31. A Knowledge Graph: an imagined representation
Nodes = entities, concepts, phrases
Lines/edges = relationships
KG’s underlying tech = Semantic Web-based Technologies
- all Worldwide Web Consortium/W3C standards
Base model = RDF (Resource Description Framework)
RDF triples
/predicates
Subject Object
predicate
Value
predicate
Patient X Loss of energy
hasSymptom
38.0°ChasTemperature
Every concept and relationship has its
own identifier (URI) – less ambiguity

32. PageRank – known Webpage connections
Pre 2010
Keyword matching
Page Rank
50% space given to organic
search results

33. Connected Data – known relationships
Match data meaning with query intent
Findable in FAIR
+ Context awareness
(Location, Time of day, Device etc)
+ UX becomes more intuitive to reflect
possible query/intent
Search Results and Discovery
Information Boxes (auto publishing)
Q & As
Searches related to “your query”
10% space given to organic search results
Google’s Knowledge Graph, 2010
Encoded knowledge
Machine readable
Human readable
Entity extraction
Known entities + known
relationships with other entities
(connected data)
People Organisations Places Events Products Services
meaning +
aboutness

34. known entities: Watch (product), sold where, by whom, price etc
Q & A
Information box
Online sellers
Organic search results
Ads
Retail sellers
Related searches
Location,
Device,
Time of Day

35. known entities: Watch (product), sold where, by whom, price etc
Q & A
Information box
Online sellers
Organic search results
Ads
Retail sellers
Related searches
Location,
Device,
Time of Day
30% of websites now help
Google with the meaning of
their websites by adding
(RDF) Schema.org and
microformats
Includes for populating:
Q&As, Info Box, rich snippets
etc

36. known entities: Virus, infectious agent, diseases caused, related symptoms
no Ads
Request for feedback
Top stories
Help & information
Safety tips
Organic search results
Trusted sources

37. Organisations can use KGs to make
data more findable too
(web-base applications)

38. Make data smarter by Semantic Annotation
Tabular data
Metadata “B”
Info model Z
RDBMs
Metadata ”A”
Info model Y
Archived dataDocuments
RDF
1. Data connectors – different data sources and formats
2. NLP (NER) & ML: processing & enrichment pipeline
3. Standardized metadata
4. Data semantically annotated (consistently) with concepts +
relationships from the KG
5. Semantic layer over all data sources: Interoperable (FAIR)
Elastic
KG

39. Make search smarter: analyse user behaviour
Tabular data
Metadata “B”
Info model Z
RDBMs
Metadata ”A”
Info model Y
PDFsDocuments
RDF
1. Search & click log analysis
2. ML: Learning to Rank
3. Detect new concepts for the KG
Kibana

40. How do you make a Knowledge Graph?

41. 2-D elements of a 3-D Knowledge Graph
Polyhierarchical Taxonomy / Thesaurus Ontology
Main domain class relationships
“Business rules”
Information model
1. Synonyms, acronyms, abbreviations
2. Language from different perspectives (not just keyword match)
3. Use standard terminologies /pick&mix
4. Different spoken languages
5. Cultural accessibility & interoperability (FAIR)
6. UX typing = autocomplete
7. UX navigation - polyhierarchical
hasSymptom
hasSymptom
Each concept and relationship has a unique URI –
helps prevent ambiguity when searching:
Cold (common cold)
Cold (temperature, feeling)

42. Semantic Annotation: Content v Datasets
• Content (unstructured text) holds more concepts with which to
refer to the knowledge graph
• Exploit 80% of unstructured text in EHRs
• Connect & index various sources through one search interface – see
the latest research and guidelines for the treatment of a disease or
condition
• Patient summary
• Datasets/Databases have fewer concepts, often unclear field names
(reference or shortened text)
• Harder to find data from keyword matching alone
• Knowledge graphs can add a higher level meaning to datasets, if not
just the metadata is indexed
• E.g. “Hypertension” – all datasets with a systolic blood
pressure over 160mmHg

43. Different types of metadata
Concrete Abstract, fuzzy
Extracted system data:
File size, Date modified etc
Extracted from system
Extracted measurement value data:
E.g. Weight (kg), Blood pressure
Extracted from text
Specific identifiers data:
E.g. email, mobile number
Extracted from text
Named real-world entities
e.g. People names, Organisations
Types & Categories
e.g. roles, disciplines
Events & Actions
e.g. birth date,
appointment, conference
Characteristics & attributes
e.g. colour, size
Entity relationships, connections
e.g. disease symptoms, causes
Topics / Subjects
e.g. Paediatrics,
Surgery
Sentiment, opinion
e.g. positive, negative
Knowledge graphs can help with more complex searches & navigation

44. Creating a DCAT metadata catalogues
(RDF base & W3C recommended)

45. Adding metadata for datasets (with RDF)
Save/Upload pipeline
System metadata
Final_final.csv
Metadata form using metadata standards in
the background e.g. Dublin Core
Description Processing & indexing pipeline
Lineage,
versioning
Upload
Annotations,
categorisation etc
Usage notation
Form populated:
Automatically
Semi-automatically
Manually
Or a combination
User rating
Auto-populated by
referencing the KG Let users determine Dataset usefulness
Reusable (FAIR)
Comments
User can modify before uploading

46. Buy versus Build
• Buy
• Quick (initially)
• Lock in?
• Development?
• New ideas, processes, sources?
• Application centric
• Build
• Agile build
• Data centric, easier application innovation
• In control: data capture to end user consumption
• Behaviour & usage measurement / policing
• KGs used across
• Integrate also Data Sensitivity & Access control
• Flexible for new data interoperability changes
e.g. convert to RDF data
• AI (ML & NLP) readyKnowledge
Graphs
AI

47. Thank you

49. fredric.landqvist@findwise.com
https://www.linkedin.com/in/fredriclandqvist/
Peter.voisey@findwise.com
https://www.linkedin.com/in/petervoisey/