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09th December 2014
Ontologising the Health Level Seven (HL7) Standard
Dr. Ratnesh SahaySemantics in eHealth & Life Sciences (SeLS)
Insight Centre for Data AnalyticsNUI Galway, Ireland
Semantic Web Application and Tools 4 Life Science (SWAT4LS)Freie Universitaet Berlin
Germany
HL7 Ontologies• Plug & Play Electronic Patient Records (PPEPR)
– Funding: Enterprise Ireland– 2006‐2009– 2014: PPEPR‐2 – http://www.ppepr.org/– Lead by me
• HL7 OWL– Supported by HL7– 2013 ‐ ongoing – http://gforge.hl7.org/gf/project/hl7owl/– Lead by Lloyd McKenzie
2/44
Tutorial Overview Background Ontology Healthcare Interoperability
Health Level Seven (HL7) Messaging Environment Plug and Play Electronic Patients Records (PPEPR) Aligning HL7 Ontologies Context & Modularity for HL7 ontologies
3/44
Ontology ? Humans like to classify things !
Galaxies, Molecules, Genomics, Education The Latin term ontologia was first invented in 1613 by two German philosophers
Rudolf Gockel Jacob Lorhard
In context of knowledge base systems – Tom Gruber (Siri inventor !) Toward Principles for the Design of Ontologies Used for Knowledge Sharing (1993) A Translation Approach to Portable Ontology Specifications (1995)
Ontologies are „Explicit Specification of a conceptualisation.“ Tom Gruber, 1993 Agreed between groups with explicit semantics. OWL Semantics, W3C, 2004 Monotonic and make Open World Assumption (OWA). OWL Semantics, W3C, 2004 Good at Description of Reality and their mappings.
4/44
Healthcare Interoperability: Background
1986: IEEE P1157 Medical Data Interchange (MEDIX) committee introduced the concept of a common healthcare data model
1987: HL7 Version 2 1995-2005: HL7 Version 3 MEDIX work is the core of current healthcare standards (Health Level Seven
(HL7), openEHR, CEN 13606)
Health Level Seven (HL7) is the most widely deployed healthcare standard !
2000 onwards: HL7 Integration platforms End-to-End bidirectional interface development (Mirth, iWay, iNTERFACEWARE)
Very few exit for Version 3 applications
None provided interoperability between Version 2 and Version 3 applications
2004 onwards: Semantic Interoperability (Ontologies) for Healthcare Projects: Artemis, RIDE, SemanticHEALTH, SAPHIRE, ACGT, W3C HCLS, etc.
Plug and Play Electronic Patient Record (PPEPR) started end of 2006
Healthcare Vision: an Unified Electronic Healthcare Records (EHRs)
5/44
Healthcare Interoperability: Current Situation
EmergencyOncology
Radiology
Laboratory
N*(N-1) Interfaces/Alignments
6/44
Ontological Approaches
EHR1 EHR2
EHR4 EHR3
(1) current situation(2) local alignment = (n× (n-1))
EHR1 EHR2
EHR4 EHR3
(1) ideal situation(2) global alignment
EHR1 EHR2
EHR4 EHR3(1) Hybrid approach(2) global and local alignments
7/44
Example Scenario
Messages
EHR (Hospital B)
1
1
2
2
3
3
Observation Order Fulfilment Request1
Observation Order Fulfilment Request Acknowledgement2
Observation Promise Confirmation 3
4
4
5
5
4
5
Observation Order Complete (Test Results)4
EHR (Hospital A)
EHR (General Practitioner)
5 Observation Order Complete Acknowledgement
V2.6
Sean Murphy
Sean Murphy
Diabetic patients are treated with either Insulinor Avandia, but not both.
Hospital A(Drug Policy)
Sean Murphy
8/44
<xs:complexType name="AD" mixed="true"><xs:complexContent><xs:extension base="ANY">
<xs:sequence><xs:element name="country" type="adxp.country"/>
…… </xs:complexType>
HL7 Messaging Environment - 1: Semantics to Implementation
type PostalAddress alias AD specializes ANY, LIST<ADXP>{
…………};
Semantics
XMLS (Implementation Technology)
UML (Information Model )
TopM
iddleB
ottom
HL7
Version 2
HL7
Version 3
ADADXP
STED
ANY
LIST<ADXP>
10/44
Health Level Seven (HL7) Messaging Environment : - 2Schema, Alignment, and Local Policies
HL7 V3
Hor
izon
tal
Alig
nmen
tsVertical Alignments Vertical Alignments
HL7 V2<
90 complexTypes50 elements/ attributes
/>
XSD (V2)
TrialPolicy
DrugPolicy
AccessPolicy
Hospital Hospital
<90 complexTypes50 elements/ attributes
/>
XSD (V2)
<90 complexTypes50 elements/ attributes
/>
XSD (V3)
<90 complexTypes50 elements/ attributes
/>
XSD (V3)
Medium size hospital with 300 – 380 beds40,000 – 45,000 inpatients per year65,000 – 70,000 outpatient per year1000 – 1300 HL7 XSDs
DrugPolicy
BedPolicy
AccessPolicy
11/44
HL7 Messaging Environment – 3:Contextual/Modular Information Structure
Each entity is identified by an unique Object Identifiers (OIDs)
Health records are arranged in separate modules
Constraints or Policies are identifiable local modules
Hospital B
Nursing domain (5)HL7 RIM (4)
(Internal Objects)ID Schemes (1)
Patient (345678IE)
Patient ID (2)
Drug Policy (2)
Code set (2)
HL7 Internal Objects with Unique OIDs
(1) (2)(3)
Hospital A
Drug Policy (2)
Code set (1)
ID Schemes (1)
Nursing domain (5)
Patient ID (1)
Patient (678970W)
HL7 RIM (3)(Internal Objects)
HL7 Internal Objects with Unique OIDs
(1)(2)
(3)
12/44
HL7 Messaging Environment – 4:Example
<identifiedPersoncodeSystem=" 2.5.1.44.2.1 ">
<name use="L"><given>Sean</given><family>Murphy</family>
</name></identifiedPerson>
<affectedPersoncodeSystem="2.5.1.76.1.1">
<name use="L"><first>Sean</first><last>Murphy</last>
</name></ affectedPerson>
UML
XSD
XML
HL7 v3<PID.5>
<XPN.3>Sean Murphy</XPN.3><XPN.7>L</XPN.7>
</PID.5>
<PID.5><XPN use=“S”>
<XPN.1> Sean </XPN.1><XPN.2> Murphy </XPN.2>
</XPN></PID.5>
XSD
XML
HL7 v2
Context Hospital A:Patient.hasMedication (Insulin->intersection(Avandia))=isEmpty()
Drug
Policy
13/5113/44
Ontology Building Methodologies
Features Indentified Reusability of non-ontological structured resources Layering of ontologies Local adaptation of ontologies
EnterpriseOntology
METHONTOLOGY On‐To ‐Knowledge DILIGENT
Reusability +/‐ +/‐ +/‐ +/‐Layering ‐ ‐ ‐ +/‐
Local Adaptation ‐ ‐ ‐ +
15/5115/44
PPEPR Methodology
PPEPR Methodology
Methodological
Enterprise Ontology
METHONTOLOGY
On‐To‐Knowledge
DILIGENT
Empirical
Road Maps
Domain Experiences
16/5116/44
PPEPR Methodology
9. Testing
3. Language Selection
4. Development Tools
5. Lift HL7 Resources
7. Local Adaptation
Modelling Technology Support
6. Layering
1. Indentify Purpose
2. Indentify HL7 Resources
Scoping
8. Alignment
17/5117/44
Modelling: Lifting HL7 ResourcesLanguage Transformation: A Hard problem
XML Schema Ontology
Data type (1) Supports large number of data types (1) RDFS/OWL 1 has limited support, thanks to OWL 2 for extended data types support
Structure (1) Nested data structure
(2) Tree structure ( top element is root)
(3) Sequence to describe element order
(1) Concept composition is through properties
(2) Graph based (Any concept could be root)
(3) No ordering of concepts
Relation (1) Inheritance through Type and Extension
(2) No Support
(1) Multiple Inheritance
(2) Inheritance on properties and logical implications (symmetric, Transitive, etc.)
18/5118/44
Transformation Rules
MIF2OWL XSD2OWL
maximumMultiplicity|minimumMultiplicity
@maxOccurs@minOccurs
childClassextension@base|restriction@baseunion@memberTypesattribute@classCode type=Class
class | containedClasscomplexType|group|attributeGroup
typeelement@type
element@substitutionGroup
attribute element|attribute
HL7 MIF
StaticModel.association|StaticModel.attribute
Annotation@appinfohl7:LongName|hl7:Type
otherAnnotation | appInfo
OWL
ObjectProperty|DataProperty
SubPropertyOf
Range
Class
SubClassOf
max|min
Annotations@label|comment
19/5119/44
Example
<xs:simpleType name="ActClassObservation"><xs:annotation>
<xs:documentation>specDomain: S11529 (C-0-T11527-S13856-S11529-cpt)</xs:documentation> </xs:annotation> <xs:union memberTypes="ActCondition ActClinicalTrial ActSpecimenObservation ActGenomicObservation "> </xs:union>
</xs:simpleType>
<xsl:for-each select="xsd:union[@memberTypes and parent::xsd:simpleType] | xsd:simpleContent/xsd:union[@memberTypes and parent::xsd:simpleContent“ ]
<xsl:if test="@memberTypes"> <xsl:for-each select="tokenize(@memberTypes, '\s')">
Class: <xsl:value-of select="." /> SubClassOf:
<xsl:value-of select="$currentClass"/> </xsl:for-each>
Class: ActCondition SubClassOf: ActObservationClass: ActClinicalTrial SubClassOf: ActObservationClass: ActSpecimenObservation SubClassOf: ActObservationClass: ActGenomicObservation SubClassOf: ActObservation
20/5120/44
Example
<xs:complexType name="Patient"><xs:sequence>
<xs:element maxOccurs="unbounded" minOccurs="1" name="id" type="II"/> <xs:element maxOccurs="1" minOccurs="1" name="name" type="EN"/> <xs:element maxOccurs="1" minOccurs="1" name="administrativeGenderCode" type="CE"/> <xs:element maxOccurs="1" minOccurs="1" name="birthTime" type="TS"/> <xs:element maxOccurs="unbounded" minOccurs="1" name="addr" type="AD"/> .....
</xs:sequence> <xs:attribute fixed="PSN" name="classCode" type="EntityPerson" use="optional"/>
</xs:complexType>
Class: <xsl:value-of select="$currentClass"/><xsl:for-each select="xsd:attribute[@name="classCode"]
<xsl:if test="@name='classCode'"> SubClassOf: <xsl:value-of select="@type"/>
</xsl:for-each>
Class: Patient SubClassOf: EntityPersonObjectProperty: id Domain: Person Range: IIObjectProperty: name Domain: Person Range: ENObjectProperty: administrativeGenderCode Domain: Person Range: CEObjectProperty: birthTime Domain: Person Range: TSObjectProperty: addr Domain: Person Range: AD
21/5121/44
Layering of Ontologies
Bottom
-upTop-dow
n
×LocalOntology Local
Ontology
GlobalOntology(HL7 V2)
GlobalOntology(HL7 V3)
×
HL7 V2(coreSchemas)
HL7 V3(coreSchemas) (1) Datatype
(2) Vocabulary(common for all hospitals)
HL7 V2 XSD(1) HL7 V2 XSD(2) HL7 V3 XSD(1) HL7 V3 XSD(2)
MessageOntology
MessageOntology
MessageOntology
MessageOntology
+ +
Message Schema(hospital-specific)
Lifting
Global Alignment
Lifting
Merging
Local Alignment
22/44
Local Ontology: Merging Local Ontologies
Class: ObservationRequestSubClassOf: ActObservation
Class: SpecimenObservationSubClassOf: ActObservation
Class: Observer SubClassOf: RoleClass
Class: DiabeticType2ObservationSubClassOf: SpecimenObservation
Class: ObservationOrder.POOB_MT210000UVSubClassOf: ActObservation
Class: Observer.POOB_MT210000UVSubClassOf: RoleClass
Class: HemoglobinObservation.POOB_MT210000UVSubClassOf: ActObservation
Class: ObservationRequest SubClassOf: ActObservation
Class: SpecimenObservation SubClassOf: ActObservation
Class: Observer SubClassOf: RoleClass
Class: HemoglobinObservation.POOB_MT210000UV SubClassOf: ActObservation
Class: DiabeticType2Observation SubClassOf: SpecimenObservationHemoglobinObservation.POOB_MT210000UV
=
=
⊑
+
23/5123/44
Alignment: HL7 Global and Local Ontologies
HL7 v3
GLO
BAL
LOCA
L
HL7 v2
PID
PDI
XAD
XON
XPN.1PID.5
XPN.2
Person
Role
Organisation
Ad
classCode
FirstName
Uni. Hospital
Name
LabTestOrder
IdName
Pub. Hospital
OBX1.2
identification
GLO
BAL
LOCA
L
First Name LabTestOrder
25/44
Alignment: Example
Class: ObservationRequest SubClassOf: ActObservationClass: SpecimenObservation SubClassOf: ActObservationClass: Observer SubClassOf: RoleClass
Class: DiabeticType2Observation SubClassOf: SpecimenObservation
Class: ObservationOrder.POOB_MT210000UVSubClassOf: ActObservationClass: Observer.POOB_MT210000UV SubClassOf: RoleClass
Class: HemoglobinObservation.POOB_MT210000UVSubClassOf: ActObservation
Class: ADObjectProperty: AD.1 Domain: AD Range: AD.1.CONTENTObjectProperty: AD.2 Domain: AD Range: AD.2.CONTENTObjectProperty: AD.3 Domain: AD Range: AD.3.CONTENT
Class: AD SubClassOf: ANYObjectProperty: streetAddressLine Domain: AD Range: Adxp.countryObjectProperty: state Domain: AD Range: Adxp.stateObjectProperty: city Domain: AD Range: Adxp.city
<xsd:complexType name="AD.3.CONTENT"><xsd:annotation>
<xsd:appinfo> <hl7:Type>ST</hl7:Type> <hl7:LongName>City</hl7:LongName>
</xsd:appinfo> </xsd:annotation>
Version 3 Version 3
Version 3 Version 2
HL7 Annotation
26/5126/44
Ontology Alignment Tools
Method/Tool HL7 (V3 –V3) precision‐recall(Local Ontologies)
HL7 (V2‐V3) precision‐recall(Global/Local Ontologies)
Threshold Value
Falcon‐AO 70%(p)‐60%(r)70%(p)‐50%(r)70%(p)‐50%(r)
30%(p)‐30%(r)30%(p)‐20%(r)30%(p)‐20%(r)
0.1‐0.40.4‐0.70.7‐1
H‐Match 80%(p)‐100%(r)80%(p)‐90%(r)80%(p)‐90%(r)
40%(p)‐30%(r)40%(p)‐20%(r)40%(p)‐20%(r)
0.1‐0.40.4‐0.70.7‐1
BLOOMS 90%(p)‐40%(r)90%(p)‐30%(r)90%(p)‐30%(r)
90%(p)‐20%(r)90%(p)‐10%(r)90%(p)‐10%(r)
0.1‐0.40.4‐0.70.7‐1
RiMOM 60%(p)‐100%(r)70%(p)‐90%(r)70%(p)‐90%(r)
40%(p)‐40%(r)40%(p)‐40%(r)30%(p)‐20%(r)
0.1‐0.40.4‐0.70.7‐1
AgreementMaker 70%(p)-100%(r)70%(p)-90%(r)70%(p)-90%(r)
40%(p)-50%(r)40%(p)-50%(r)30%(p)-20%(r)
0.1‐0.40.4‐0.70.7‐1
27/44
Alignment: SPARQL Recipes
CONSTRUCT { ?v3 owl:equivalentClass ?v2 }WHERE { ?v3 rdf:type owl:Class . ?v2 rdf:type owl:Class .
?v2 rdfs:label ?LongName . {FILTER regex(str(?v3), str(?LongName), ``i'')}}
CONSTRUCT { ?v3 owl:equivalentProperty ?v2 }WHERE { ?v3 rdf:type owl:ObjectProperty .
?v2 rdf:type owl:ObjectProperty . ?v2 rdfs:range ?v2range .?v3 rdfs:range ?v3range . ?v2 rdfs:domain ?v2domain . ?v3 rdfs:domain ?v3domain .?v2range owl:equivalentClass ?v3range . ?v2domain owl:equivalentClass ?v3domain };
Class Matching
Property Matching
28/44
Alignment: SPARQL Recipes
Method/Tool HL7 (V3 –V3) precision–recall(Local Ontologies)
HL7 (V2‐V3) precision‐recall(Global/Local Ontologies)
Threshold Value
Falcon‐AO 70%(p)‐60%(r)70%(p)‐50%(r)70%(p)‐50%(r)
30%(p)‐30%(r)30%(p)‐20%(r)30%(p)‐20%(r)
0.1‐0.40.4‐0.70.7‐1
H‐Match 80%(p)‐100%(r)80%(p)‐90%(r)80%(p)‐90%(r)
40%(p)‐30%(r)40%(p)‐20%(r)40%(p)‐20%(r)
0.1‐0.40.4‐0.70.7‐1
BLOOMS 90%(p)‐40%(r)90%(p)‐30%(r)90%(p)‐30%(r)
90%(p)‐20%(r)90%(p)‐10%(r)90%(p)‐10%(r)
0.1‐0.40.4‐0.70.7‐1
RiMOM 60%(p)‐100%(r)70%(p)‐90%(r)70%(p)‐90%(r)
40%(p)‐40%(r)40%(p)‐40%(r)30%(p)‐20%(r)
0.1‐0.40.4‐0.70.7‐1
AgreementMaker 70%(p)‐100%(r)70%(p)‐90%(r)70%(p)‐90%(r)
40%(p)‐50%(r)40%(p)‐50%(r)30%(p)‐20%(r)
0.1‐0.40.4‐0.70.7‐1
SPARQL Recipes 80%(p)‐90%(r) 50%(p)‐60%(r) NA
Extend alignment tools (AgreementMaker, RiMOM) by including domain-specific thematic structures instead of general information structures like WordNet, Wikipedia, DBpedia 29/44
Example Scenario
PPEPR
Observation Order Fulfilment RequestMessages
Observation Order Fulfilment Request AcknowledgementObservation Promise Confirmation Observation Order Complete (Test Results)
EHR (Hospital B)
1
1
2
2
3
3
123
4
4
5
5
4
5
45
EHR (Hospital A)
EHR (General Practitioner)
Class: rxnorm:AvandiaSubClassOf: galen:Drug
Class: rxnorm:InsulinSubClassOf: galen:Drug
EquivalentProperties:HospitalA:hasMedicationHospitalB:hasTreatment
DisjointClasses:HospitalA:hasMedication some rxnorm:AvandiaHospitalA:hasMedication some rxnorm:Insulin
Hospital Drug Policy
Sean HospitalA:hasMedication rxnorm:InsulinSean HospitalB:hasTreatment rxnorm:Avandia
Inconsistency
Observation Order Complete Acknowledgement
31/44
Where is the Fault ?
Ontologies are „Specification of a conceptualization.“ Tom Gruber, 1993
Agreed between groups with explicit semantics. OWL Semantics, W3C, 2004
Monotonic and make Open World Assumption (OWA). OWL Semantics, W3C, 2004
Good at Description of Reality and their mappings.
Ontology are not Model of local and context-specific information Model of time-dependent information Model of context-specific constraints (e.g., policy, preferences)
and validation
32/44
State-OF-The-Art -1 : Formal Approaches
We did investigation for support of five features Context-awareness (CA) Modularity (M) Profile and policy management (P & PM) Correspondence expressiveness (CE) Robustness to heterogeneity (RH)
Considered Approaches: Standard DL: Web Ontology Language (OWL)
No localised or contextualised semantics Reusability or knowledge integration is limited to owl:imports
Context-Extensions of DLs : Distributed Description Logic (DDL) Packet Description Logic (PDL) Integrated Distributed Description Logic (iDDL) E-connection
DL+Constraints/Rules DL+DL-Safe Rules Database-Style Integrity Constraints (IC) within OWL (OWL/IC in Pellet)
Rule-based Modular Web Rule Bases
Query-Based Query-Translation
Repairing and Reasoning with Inconsistencies (DeLP)
NONE OF THEM ADDRESSES ALL FEATURES
33/44
State-of-the-Art-2 (RDF)
Resource Description Framework (RDF) RDF is an assertional logic (antecedent or premises is always true), where each triple
expresses a simple proposition. [W3C RDF Semantics document]– In result, triple (s p o) represent facts, notion of “universal truth”.– RDF triples are context-free
Reification N statements about a statement Good for making statements about provenance NO coupling with the truth of the triple that has been reified Cannot relate the truth of a triple in one context (graph) to another
Named Graphs Assigned an ID (URI) to each graph Good for making statements about provenance Associate named graphs with triples
– Triples become quadruples – Fourth element is the URI of the named graph (origin)
Similar to Reification for the “truth of a triple” N3-Context
Similar to Reification as far as “truth of a triple” is concerned
34/44
Standard Semantics : OWL
O=⟨T,A⟩ {0= ontology, T=Tbox, A=Abox}
Class: rim:RolePatientSubClassOf: rim:Role
Class: HA:IrishPPSIdSubClassOf: rim:EntityIdentification
Class: HA:LabTestOrderSubClassOf: rim:Act
Class: HA:HemoglobinTestSubClassOf: rim:Act
Class: galen:PatientSubClassOf: galen:HumanClass: HB:OrderLabObservationSubClassOf: galen:OrderActObjectProperty: HB:hasTreatment
DisjointClasses:HA:hasMedication some rxnorm:AvandiaHA:hasMedication some rxnorm:Insulin
Class: rxnorm:AvandiaSubClassOf: galen:Drug
Class: rxnorm:InsulinSubClassOf: galen:Drug
THA THB=
=
=
35/5135/44
Distributed Description Logic (DDL)
Oi=⟨Ti,Ai, rij⟩ {0i= ontology, Ti=Tbox, Ai=Abox, rij = Bridge Rules}
HA:( HA:hasMedication some rxnorm:Insulin ) ⊑ HB:( HB:hasTreatment some rxnorm:Insulin )HA:( not HA:hasMedication some rxnorm:Avandia ) ⊑ HB:( not HB: hasTreatment some rxnorm:Avandia)
Class: rim:RolePatientSubClassOf: rim:Role
Class: HA:IrishPPSIdSubClassOf: rim:EntityIdentification
Class: HA:LabTestOrderSubClassOf: rim:Act
Class: HA:HemoglobinTestSubClassOf: rim:Act
Class: galen:PatientSubClassOf: galen:HumanClass: HB:OrderLabObservationSubClassOf: galen:OrderActObjectProperty: HB:hasTreatment
DisjointClasses:HA:hasMedication some rxnorm:AvandiaHA:hasMedication some rxnorm:Insulin
Class: rxnorm:AvandiaSubClassOf: galen:Drug
Class: rxnorm:InsulinSubClassOf: galen:Drug
THA THB=
=
=
36/5136/44
Packet Description Logic (PDL)
Oi=⟨Ti,Ai⟩ {0i= ontology, Ti=Tbox, Ai=Abox}
Class: ( HA:HemoglobinTest and (rim:measures some loinc: 4545-4) )EquivalentTo: ( galen:BloodSugarTest and (HB:hasCode some snomed: 43396009) )
Class: rim:RolePatientSubClassOf: rim:Role
Class: HA:IrishPPSIdSubClassOf: rim:EntityIdentification
Class: HA:LabTestOrderSubClassOf: rim:Act
Class: HA:HemoglobinTestSubClassOf: rim:Act
Class: galen:PatientSubClassOf: galen:HumanClass: HB:OrderLabObservationSubClassOf: galen:OrderActObjectProperty: HB:hasTreatment
DisjointClasses:HA:hasMedication some rxnorm:AvandiaHA:hasMedication some rxnorm:Insulin
Class: rxnorm:AvandiaSubClassOf: galen:Drug
Class: rxnorm:InsulinSubClassOf: galen:Drug
THA THB=
=
=
37/5137/44
Database Style-IC
O=⟨Tn, TC, A⟩ {0= ontology, Tn =Normal Tbox,TC = Constraint Tbox, A=Abox}
Class: rim:RolePatientSubClassOf: rim:Role
Class: HA:IrishPPSIdSubClassOf: rim:EntityIdentification
Class: HA:LabTestOrderSubClassOf: rim:Act
Class: HB:HemoglobinTestSubClassOf: rim:Act
Class: galen:PatientSubClassOf: galen:HumanClass: HB:OrderLabObservationSubClassOf: galen:OrderActObjectProperty: HB:hasTreatment
DisjointClasses:HA:hasMedication some rxnorm:AvandiaHA:hasMedication some rxnorm:Insulin
Class: rxnorm:AvandiaSubClassOf: galen:Drug
Class: rxnorm:InsulinSubClassOf: galen:Drug
Tn(HA)=
=
=
Tn(HB)
TC(HA)
38/5138/44
Feature Comparisons
Context‐awareness
Modularity Profile & PolicyManagement
DL/OWL ‐ ‐/+ ‐
DDL/C‐OWL + + ‐
P‐DL + + ‐
DDL Revisited + + ‐
IDDL + + ‐
E‐connection + + ‐
RDFS‐C (Guha’s) + ‐/+ ‐
Query‐based ‐/+ ‐ ‐
Modular Rule bases + + ‐/+
OWL/IC ‐ ‐/+ ‐/+
DeLP/Paraconsistent ‐ ‐ ‐/+
39/44
Feature Comparisons
C.A M. P. & PM C.E R.H
DL/OWL ‐ ‐/+ ‐ Good Very Limited
DDL/C‐OWL + + ‐ Very Good Good
P‐DL + + ‐ Very Limited Limited
DDL Revisited + + ‐ Very Good Medium
IDDL + + ‐ Good Very Good
E‐connection + + ‐ Medium Excellent
RDFS‐C + ‐/+ ‐ Good Good
DeLP/Paraconsistent ‐ ‐ ‐/+ Good Good
Query‐based ‐/+ ‐ ‐ Very Good Very Good
Modular Rule bases + + ‐/+ Limited Limited
OWL/IC ‐ ‐/+ ‐/+ Good LimitedC.A: Context-awareness, M: Modularity, P & PM: Profile and policy management, CE: Correspondence expressiveness, RH: Robustness to heterogeneity
40/51
Envisioned Situation - Context & Policy aware ontological model and reasoning
GALEN SNOMED RIM
Globa
l (D)
Policy1 Policy2 Policy3 Policyn Local (P)
GALEN SNOMED RIM
Globa
l (D)
Local (P)
Hospital A Hospital B
Policy1 Policy2 Policy3 Policyn
41/5141/44
An ontology is good at the top-down modeling of a domain reduces the bilateral correspondences between healthcare applications delegates the majority of mediation to the central integration location
An ontology provides an executable (comparing to HL7 UML model) semantics and consistent model
The Semantic Web layer cake allows to engage information model, schema, and instances under a single framework. In HL7 they are represented in three isolated layers.
An automated ontology alignment is a great support for the domain experts comparing manual syntactic alignment
An ontology for the healthcare domain eases harmonising Medical, Life Sciences, and Pharma domains Prominent vocabularies are already available as ontologies (SNOMED, OBI, EFO,
RXNORM, Disease Ontology, Cell Type Ontology, etc.) An ontology has limitations in representing
Contextual and modular information Policy-based information
Summary
42/5142/44
Things cooking at the moment !
HL7 FHIR - OWL HL7 FHIR - RDF
http://www.hl7.org/implement/standards/fhir/
43/44