1. A Semantic-Web Representation of Clinical Element Models
Cui Tao, PhD
Mayo Clinic

2. Acknowledgement 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

3. EHR
SHARPn
drools
Normalized EHR
XML
Database
Schema
CEM
XML Schema
Database
OWL
Triple Store
SWRL
drools

4. Introduction Clinical Element Model (CEM):
Logical models ensure semantic interoperability for:
Data representation
Data interpretation
Data exchange within and across heterogeneous sources and applications
Represented in CEML/CDL (Constraint Definition Language)
Define syntax and grammar
Not semantics

5. Introduction Semantic Web
Explicit and formal semantic knowledge representation
Web Ontology Language (OWL) /Resource Description Framework (RDF):
Define relationships
Define classes
Define constraints
Consistency checking
Link to other domain terminologies
Harmonize with other clinical data modeling languages
Semantic reasoning

6. Detailed Clinical Element Model Ontology
Layer1:meta-ontology
Meta-CEM.OWL
Layer2: detailed clinical element models
Detailed Clinical Element Model Ontology
Layer3:patient instances
RDF Triple Store
Joe Doe’s record
Jean Doe’s record
Joe Doe’s record

7. Semantic Reasoning Consistency checking Classification and reasoning
Cardinality constraints
Data types
Property allowed domains and ranges
Permissible values in value sets
Classification and reasoning

8. Consistency Checking

9. Consistency Checking

10. Consistency Checking

11. Automatic Classification
Semantic Definition of Normal DBP Data
Two DBP Measurements

12. Automatic Classification
Semantic Definition of Normal BP
Infer that both exam1 and exam2 are within the normal range

13. Semantic Reasoning Example
Has two outpatient (if possible) measurements of SBP >140 or DBP > 90 at least one month after taking anti- hypertensive drugs.
Ontology:
“Labetalol 100mg” (SCT ) is an
anti-hypertensive drug (SCT ).
CNTRO temporal relation reasoner:
5 weeks > one month
Duration (May 1 and March 1) > one month

14. Implementation Status
Meta Ontology
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

15. CEM-OWL convertor Meta-Ontology Shared CEM Ontology
import
Shared CEM Ontology
import
CEM Core Model Ontologies
import
CEM Secondary Use Ontologies

16. Download the ontologies
Shared Models:
CORE Models:
Secondary Use: 

17. Conclusion and Future Directions
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