A Semantic-Web Representation of
Clinical Element Models
Cui Tao, PhD
Mayo Clinic
Acknowledgement
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Christopher G. Chute, MD, DrPH
Robert R. Freimuth, PhD
Stanley M. Huff, MD
Guoqian Jiang, PhD
Thomas A. Oniki, PhD
Jyotishman Pathak, PhD
Craig Parker, MD
Deepak Sharma
Feichen Shen
Sridhar D. Yadav
Qian Zhu, PhD
EHR
SHARPn
drools
Normalized EHR
Database
Database
Schema
CEM
OWL
Triple Store
SWRL
drools
XML
Schema
XML
Introduction
Clinical Element Model (CEM):
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Logical models ensure semantic interoperability for:
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Data representation
Data interpretation
Data exchange within and across heterogeneous sources and
applications
Represented in CEML/CDL (Constraint Definition Language)
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Define syntax and grammar
Not semantics
Introduction
Semantic Web
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Explicit and formal semantic knowledge representation
Web Ontology Language (OWL) /Resource Description
Framework (RDF):
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Define relationships
Define classes
Define constraints
Consistency checking
Link to other domain terminologies
Harmonize with other clinical data modeling languages
Semantic reasoning
Layer1:meta-ontology
Layer2: detailed clinical element models
Layer3:patient instances
Joe
JeanDoe’s
Doe’srecord
record
Joe Doe’s record
RDF Triple Store
Semantic Reasoning
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Consistency checking
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Cardinality constraints
Data types
Property allowed domains and ranges
Permissible values in value sets
Classification and reasoning
Consistency Checking
Consistency Checking
Consistency Checking
Automatic Classification
Semantic Definition of Normal DBP Data
Two DBP Measurements
Automatic Classification
Semantic Definition of Normal BP
Infer that both exam1 and exam2 are within the normal range
Semantic Reasoning Example
Has two outpatient (if possible) measurements of SBP >140
or DBP > 90 at least one month after taking antihypertensive drugs.
Ontology:
“Labetalol 100mg” (SCT318445000) is an
anti-hypertensive drug (SCT1182007).
CNTRO temporal relation reasoner:
5 weeks > one month
Duration (May 1 and March 1) > one month
Implementation Status
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Meta Ontology
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Basic category classes
Properties
Cardinality constraints
Two OWL experts and two CEM experts have evaluated the
meta-ontology to ensure it can faithfully cover the original
contents
Automatic Convertor: detailed CEM ontologies
CEM-OWL convertor
Meta-Ontology
import
Shared CEM Ontology
import
CEM Core Model
Ontologies
import
CEM Secondary Use
Ontologies
Download the ontologies
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Shared Models:
http://informatics.mayo.edu/sharp/CEM2OWL/CEM-Shared.owl
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CORE Models:
http://informatics.mayo.edu/sharp/CEM2OWL/COREModels/COREStandardLabObs.owl
http://informatics.mayo.edu/sharp/CEM2OWL/COREModels/COREStandardLabObsCoded.owl
http://informatics.mayo.edu/sharp/CEM2OWL/COREModels/COREStandardLabObsIntervalQuantitative.owl
http://informatics.mayo.edu/sharp/CEM2OWL/COREModels/COREStandardLabObsNarrative.owl
http://informatics.mayo.edu/sharp/CEM2OWL/COREModels/COREStandardLabObsOrdinal.owl
http://informatics.mayo.edu/sharp/CEM2OWL/COREModels/COREStandardLabObsQuantitative.owl
http://informatics.mayo.edu/sharp/CEM2OWL/COREModels/COREPatient.owl
http://informatics.mayo.edu/sharp/CEM2OWL/COREModels/CORENotedDrug.owl
http://informatics.mayo.edu/sharp/CEM2OWL/COREModels/COREDiseaseDisorder.owl
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Secondary Use:
http://informatics.mayo.edu/sharp/CEM2OWL/SecondaryUse/SecondaryUseDiseaseDisorder.owl
http://informatics.mayo.edu/sharp/CEM2OWL/SecondaryUse/SecondaryUseNotedDrug.owl
http://informatics.mayo.edu/sharp/CEM2OWL/SecondaryUse/SecondaryUsePatient.owl
http://informatics.mayo.edu/sharp/CEM2OWL/SecondaryUse/SecondaryUseStandardLabObsCoded.owl
http://informatics.mayo.edu/sharp/CEM2OWL/SecondaryUse/SecondaryUseStandardLabObsIntervalQuantitative.owl
http://informatics.mayo.edu/sharp/CEM2OWL/SecondaryUse/SecondaryUseStandardLabObsNarrative.owl
http://informatics.mayo.edu/sharp/CEM2OWL/SecondaryUse/SecondaryUseStandardLabObsOrdinal.owl
http://informatics.mayo.edu/sharp/CEM2OWL/SecondaryUse/SecondaryUseStandardLabObsQuantitative.owl
http://informatics.mayo.edu/sharp/CEM2OWL/SecondaryUse/SecondaryUseStandardLabObsTiter.owl
Conclusion and Future Directions
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Meta-Ontology: semantically defined the basic classes,
properties, their relationships, and constraints
Convertor: CDLOWL
Represent SHARPn normalized data using RDF
Investigate SWRL/Drools combination for phenotyping