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:– 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

Clinical Research ProtocolEligibility Criteria:

- Inclusion- Exclusion

EMR DATA

Meds Procedures

Diagnoses Demographics

…FailPassPass5/8 criteria met

Yes0033333

…………………

Pass

Pass

Criteria #3

(Pass/Fail/ Researcher Needs to Evaluate)

…

…

…

FailPass3/8 criteria met

No 0022222

Pass

No Criteria #2

(Pass/Fail/ Researcher Needs to Evaluate)

Pass 6/8 criteria met

Yes0011111

Criteria #1

(Pass/Fail/ Researcher Needs to Evaluate)

# Criteria Met / Total Criteria in Protocol

Potentially Eligible for Protocol

Patient MR #

Research Coordinator selects protocol for patient screening:

Research Coordinator views list of patients and selects which ones to approach in person for evaluation and recruitment.

Clinical Evaluation and Recruitment

Research Eligibility Screening Use Case, 9-24-2007

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– UHCL-1 transcribed_into Dardarin

• Using instance-of vs subclasses– UHCL-1 subclass-of Gene vs UHCL-1 instance-of Gene

• Granularity/Specificity of relationships– AllelicVariant causes Disease, vs LRR2KVariant causes Parkinson’s Disease

• Uncertainty– The discovery that genetic mutations in the alpha synuclein gene could cause Parkinson's disease in

families

• Multiple Domains/Ranges– Property: associated_with, Domains: Pathway, Protein, Ranges: Cell, Biomarker

• Default Values– Default function of proteosomal pathway is protein degradation

• Ontology Inclusion and Modularization– 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

– BioRDF– BIONT– URIs– Rules ...

• Development– BioRDF– BIONT– URIs– Rules

• Secondary Uses Of Healthcare Data– BioRDF– BIONT– URIs– Rules …