Outline • Goals • Use Cases • Ontologies • Best Practices and Modeling Issues • Collaboration with BioRDF • Next Steps: – Clinical Observations Interoperability.

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Transcript Outline • Goals • Use Cases • Ontologies • Best Practices and Modeling Issues • Collaboration with BioRDF • Next Steps: – Clinical Observations Interoperability.

Outline
• Goals
• Use Cases
• Ontologies
• Best Practices and Modeling Issues
• Collaboration with BioRDF
• Next Steps:
– Clinical Observations Interoperability
BIONT Goals
• Develop best practices around crucial questions related to
creation and use of ontologies:
– What is an ontology?
– How should one represent information in an ontology?
– Ontology lifecycle: How should ontologies be created, used, accessed,
maintained and evolved?
• Develop use cases spanning the Bench to Bedside Spectrum
• Identify best practices and methodologies in design and
development of various ontologies across the Bench to Bedside
Spectrum
• Collaboration with various Task Forces: BioRDF, ACPP, SWAN
Use Cases
• Parkinson’s Disease Use Case
• Combined AD – PD Use Case
• Patient Recruitment Use Case
Use Case: Parkinson’s Disease
• Description of Parkinson’s Disease and Information Needs from
different perspectives:
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–
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Systems Physiology View
Cellular and Molecular Biologist View
Clinical Researcher View
Clinical Guideline Formulator View
Clinical Decision Support Implementer View
Primary Care Clinical View
Neurologist View
• Available at:
– http://esw.w3.org/topic/HCLS/ParkinsonUseCase
• Developed by:
– Don Doherty
– Ken Kawamato
Research Eligibility Screening Use Case,
Research Coordinator
selects protocol for patient
screening:
9-24-2007
Clinical Research Protocol
Eligibility Criteria:
- Inclusion
- Exclusion
EMR DATA
Meds
Research
Coordinator
views list of
patients and
selects which
ones to approach
in person for
evaluation and
recruitment.
Clinical
Evaluation and
Recruitment
Diagnoses
Procedures
Demographics
Patient MR #
Potentially
Eligible for
Protocol
# Criteria
Met / Total
Criteria in
Protocol
Criteria #1
(Pass/Fail/
Researcher
Needs to
Evaluate)
No Criteria #2
(Pass/Fail/
Researcher
Needs to
Evaluate)
Criteria #3
(Pass/Fail/
Researcher
Needs to
Evaluate)
…
0011111
Yes
6/8 criteria
met
Pass
Pass
Pass
…
0022222
No
3/8 criteria
met
Pass
Fail
Pass
…
0033333
Yes
5/8 criteria
met
Pass
Pass
Fail
…
…
…
…
…
…
…
…
Ontologies
• Biomedical Research
– Parkinson’s Disease Ontology
• Clinical Reserch
– Problem Oriented Medical Record Ontology
– Study Data Tabulation Model
• Clinical Practice
– Detailed Clinical Models
– ACPP Ontology
Parkinson’s Disease Ontology
POMR Ontology
ACPP Ontology
Collaboration with BioRDF
• Collaboration and contribution to the joint AD –
PD Use Case
• BIONT:
– Top Down (Domain?) Ontology Construction
– Driven by Use Case
• BioRDF:
– Bottom up (Data?) driven Ontology construction
– Driven by the need to provide a thin layer to
“integrate” the islands
Design Issues in Parkinson’s
Ontology
•
Using classes vs relationships
–
•
Using instance-of vs subclasses
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Default function of proteosomal pathway is protein degradation
Ontology Inclusion and Modularization
–
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Property: associated_with, Domains: Pathway, Protein, Ranges: Cell, Biomarker
Default Values
–
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The discovery that genetic mutations in the alpha synuclein gene could cause Parkinson's disease in
families
Multiple Domains/Ranges
–
•
AllelicVariant causes Disease, vs LRR2KVariant causes Parkinson’s Disease
Uncertainty
–
•
UHCL-1 subclass-of Gene vs UHCL-1 instance-of Gene
Granularity/Specificity of relationships
–
•
UHCL-1 transcribed_into Dardarin
NeuroNames, Enzyme Commission, MeSH
Higher Order Relationships
–
Association between a Gene and a Disease in the context of a Study
Best Practices for POMR Ontology
• Mappings to foundational ontologies to facilitate
ontological commitment
• Adoption of consient terminology for ontological
constructs
• Avoidance of constructs which denote cognitive
representations (skos:Concept)
• Careful use of partial and complete class axioms
• Clear seperation of temporal semantics
• Exhaustive disjointeness
Problems and Issues for POMR
Ontology
• Expressing periodic time intervals with OWL Time
• No known URI-based naming convention (or OWL
export) for SNOMED CT terms
• Lossy semantic transformation from HL7 to RDF
• No feasible means of reasoning over very large
ontologies (GALEN, DOLCE, etc..)?
Hard issues
• What level of model becomes a Java class?
• How do you make models easy to use in Java?
• Opposition to this level of detailed models
• Modeling of concepts and quantitative values in a single
language/paradigm
• Huge diversity of modeling styles: how to be consistent?
• Defining computable connections between model and
externally defined terminology
• Large number of models needed
Next Steps: Clinical Observations
Interoperability
• Information Models
– DCM
– SDTM
– BRIDG
• Terminologies
– Snomed
– NCI Thesaurus
– MedDRA
• Re-use and alignment of these models for
interoperability
Some tentative proposals
• Two Organizations of Activity
• Technology Driven Organization
– BioRDF
– BIONT
• Application Driven Organization
– ACPP
– SWAN
– Scientific Publishing
Proposed Reorg?
• Discovery
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BioRDF
BIONT
URIs
Rules ...
• Development
–
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–
–
BioRDF
BIONT
URIs
Rules
• Secondary Uses Of Healthcare Data
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BioRDF
BIONT
URIs
Rules …