Presented at Health Informatics New Zealand (HINZ 2017) Conference, 1-3 Nov 2017, Rotorua, New Zealand. Authorship: Koray Atalag, Reza Kalbasi, David Nickerson The University of Auckland

1. A Semantic Web based Framework for Linking
Healthcare Information with Computational
Physiology Models
Koray Atalag, Reza Kalbasi, David Nickerson
The University of Auckland
Koray Atalag MD, PhD, FACHI
k.atalag@auckland.ac.nz
Senior Research Fellow, ABI
Management Board Member, openEHR Foundation
Chief Information Officer, The Clinician

2. Outline
• Problem / Research Question
• Intro to Computational Physiology
• Linking both Domains
• Semantic Web and Ontology Mapping
• Results
• Discussion

3. Problem / Research Question
• Computational models have great potential to improve healthcare
• Lots of real-world data information stored in EHRs
– however discovery and reuse is poor
• Linking the two domains requires shared semantics
– Ontology/Terminology used in CP are different from Healthcare
– Semantic Web has little use in healthcare IT/informatics
Can we map knowledge sources from both domains to enable
discovery and reuse of clinical data and computational models?
Ontology mapping is hard!

4. What’s Computational Physiology
Governing equations
(laws of physics)
Anatomy & structure
High-performance
computing
Software
Material properties
from measurement
Observed function
Validation
Predicted function
Mechanistic insight

5. Tissue
Osteon NephronAcinus Liver lobuleLymph nodeCardiac sheets
Organ
Heart Lungs Diaphragm Colon EyeKnee Liver
Environment
Organ system
Organism
Cell
Protein
Gene
Atom
Network

6. Human Physiome Project &
Virtual Physiological Human (VPH)
a methodological and technological framework
that will enable collaborative investigation
of the human body as a single complex system
Descriptive, Integrative And Predictive

7. Computational Physiology of the Heart
Workflow:
cardiac imaging
to patient-care
Image Segmentation
OpenCMISS-Zinc, cmgui, VTK,
ITK, CIM,
Matlab, 3D Slicer
Biomechanics Simulation
End-SystoleDiastasis
Pressure,
contractile
force
OpenCMISS, Continuity, FEBio Ansys,
ABAQUS
Parameter ID/Calibration
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222
3
2
2
1
2
2where
  11  Caa TT
OpenCMISS, Matlab, Python,
Prediction
Stress
(kPa)
Fibre strain
OpenCMISS, Continuity, Chaste, Abaqus
Diseased
Normal
Subject-specific modelling
OpenCMISS-Zinc, CIM, CAP

8. Photo by Jerry Bunkers
Diagnosis
Measurement
Computational Physiology of the Breast
Breast image analysis
workflow

9. Why Linked Health Data?

10. Computational Modelling: CellML
• CellML includes information about:
– Model structure (how the parts of a model are organizationally related
to one another);
– Mathematics (equations describing the underlying biological
processes);
– Metadata (additional information about the model that allows
scientists to search for specific models or model components in a
database or other repository).
• CellML includes mathematics and metadata by leveraging existing
XML-based languages, such as Content MathML, XML Linking
Language (XLink), and Resource Description Framework (RDF).
(C. M. Lloyd, M. D. B. Halstead, and P. F. Nielsen, "CellML: its future, present and past" Progress in Biophysics & Molecular Biology, vol. 85, pp. 433-450, June-July 2004)

11. (www.cellml.org)
Cuellar AA, Lloyd CM, Nielsen PF, Halstead MDB, Bullivant DP, Nickerson DP, Hunter PJ. An overview of CellML 1.1, a biological model description
language.SIMULATION: Transactions of the Society for Modeling and Simulation, 79(12):740-747, 2003
Physiome Standards and Tooling

12. Semantic web
• The meaning of data, or semantics, is the
target of semantic web
• Subject - Predicate - Object
• W3C languages: RDF/RDFS/OWL etc.
• Machine processable Web
• More enhanced search results, meaning-based
data integration, reasoning services
• Healthcare semantic annotations
• Computational physiology models with
embeded semantic annotations Semantic web components defined by (Hebeler et al. 2009)

13. Ontologies
• Ontologies are formal descriptions of knowledge enabling sharing
and reuse.
• Ontologies provide:
– Classing mechanisms (multiple inheritance, subclassing, domain,
range, …);
– Class expressions (union, intersection, complement, …);
– Class axioms (one of, disjoint with, equivalence, …);
– Property characteristics (transitive, symmetric, functional, …);
– Cardinality (minimum, maximum, exactly);
– Rich set of primitive data types (string, boolean, integer, real,
datetime, URI, …);
– Management (imports, versions, compatibility, …).

14. Computational Models and Ontologies
• We are developing ontologies for physiological form and
function, and the CellML modelling language.
• Other groups are also developing ontologies relevant to
biological modelling:
– Anatomical (Gene Ontology/GONG, FMA);
– Gene regulation pathway (Gene Ontology/GONG, BioPax);
– Gene expression (Gene Ontology/GONG);
– Common access to bioinformatics sources (TAMBIS/TaO);
– Physical and mathematical (SBO, Stanford Knowledge Systems, OPB).
• We are linking model entities in the CellML repository to
our own and other ontologies.

16. List of Ontologies in our OLS
• OPB
• FMA
• CHEBI
• Gene Ontology (GO)
• Cell Ontology (CL)
• Phenotypic quality (PATO)
• Protein Ontology (PR)
• PRIDE
• EDAM
• EFO
• PROBONTO
• OBO relations Ontology
• SNOMED CT
• LOINC
• ICD (TODO)

17. • Open access specs & tooling for representing healthcare
data, enabling interoperability and building EHR
• Supports very elaborate DCM development (=Archetypes)
• Scope is full EHR - not just health information exchange
• Not-for-profit organisation - established in 2001
• Based on 20+ years of international research and practice
• Also an ISO/CEN standard (ISO 13606)
• Big international community
• All DCMs are available from: http://openehr.org/ckm
www.openehr.org

18. Body Weight Archetype

19. Semantics in openEHR
• Whole-of-model meta-data:
– Description, concept references (terminology/ontology), purpose, use,
misuse, provenance, translations
• Item level semantics (Schema level)
– Trees/Clusters (Structure)
– Leaf nodes (Data Elements)
Formally: different types of terminology bindings:
1) linking an item to external terminology/ontology for the purpose of
defining its real-world clinical/biological meaning
2) Linking data element values to external terminology (e.g. a RefSet
or terminology query)
AlsoInstance level semantic annotations – applies to actual
data collected

20. mindmap representation of openEHR Archetype
1) Linking items to SNOMED to define clinical meaning

21. 2) Linking data element values to external terminology

22. openEHR Lab Test Archetype - SNOMED & LOINC encoded

23. Realising the Physiome / VPH

24. Ontology mapping
• Ontology; formal specification of a domain
knowledge
• Ontology mapping; the definition of
corresponding objects (entities) from one
domain ontology to the other domain
ontology
• Ontology matching may appear to be virtually
impossible
• Solution: semi-automated collaborative
ontology mapping via user interaction (Web
2.0)

25. Mappings: Biophysical ⇐⇒ Clinical
Concepts

26. CellML Model > Clinical Data Discovery

27. PhysioMedApp

28. Acknowledgements
David NickersonReza Kalbasi
Peter Hunter
Tommy Yu