Ontologising the Health Level Seven (HL7) Standard

Ontologising the Health Level Seven (HL7) Standard
Dr. Ratnesh Sahay
Semantics in eHealth & Life Sciences (SeLS)
Insight Centre for Data Analytics
NUI Galway, Ireland
Semantic Web Application and Tools 4 Life Science (SWAT4LS)
Freie Universitaet Berlin
Germany
09th December 2014

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
Emergency
Oncology
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
4
Hospital A
(Drug Policy)
Messages
EHR (Hospital B)
1
1
2
2
3
3
V2.6
EHR (Hospital A)
1 Observation Order Fulfilment Request
2 Observation Order Fulfilment Request Acknowledgement
3 Observation Promise Confirmation
4
5
5
4
5
4 Observation Order Complete (Test Results)
EHR (General
Practitioner)
5 Observation Order Complete Acknowledgement
Sean Murphy
Sean Murphy
Diabetic patients are treated with either Insulin
or Avandia, but not both.
Sean Murphy
8/44

Health Level Seven Standard (HL7)

HL7 Messaging Environment - 1:
Semantics to Implementation
Semantics
type PostalAddress alias AD specializes ANY, LIST<ADXP> {
……
……};
UML (Information Model )
XMLS (Implementation Technology)
<xs:complexType name="AD" mixed="true">
<xs:complexContent>
<xs:extension base="ANY">
<xs:sequence>
<xs:element name="country" type="adxp.country"/>
……
</xs:complexType>
Top Middle Bottom
HL7 Version 2
HL7 Version 3
AD
ADXP
ST
ED
ANY
LIST<ADXP>
10/44

Health Level Seven (HL7) Messaging Environment : - 2
Schema, Alignment, and Local Policies
HL7 V3
Horizontal Alignments
Hospital Hospital
Vertical Alignments Vertical Alignments
HL7 V2
<
90 complexTypes
50 elements/ attributes
/>
XSD (V2)
Trial
Policy
Drug
Policy
Access
Policy
<
90 complexTypes
/>
XSD (V2)
<
90 complexTypes
/>
XSD (V3)
<
90 complexTypes
/>
XSD (V3)
Medium size hospital with 300 – 380 beds
40,000 – 45,000 inpatients per year
65,000 – 70,000 outpatient per year
1000 – 1300 HL7 XSDs
Drug
Policy
Bed
Policy
Access
Policy
11/44

HL7 Messaging Environment – 3:
Contextual/Modular Information Structure
Hospital B
Drug Policy (2)
Nursing domain (5)
HL7 RIM (4)
(Internal Objects)
Code set (2)
ID Schemes (1)
Patient (345678IE)
(1) (2)
(3)
Nursing domain (5)
ID Schemes (1)
Patient ID (1)
(1)
(2)
(3)
 Each entity is identified by an unique Object Identifiers (OIDs)
 Health records are arranged in separate modules
 Constraints or Policies are identifiable local modules
Patient ID (2)
HL7 Internal Objects
with Unique OIDs
Hospital A
Drug Policy (2)
Code set (1)
Patient (678970W)
HL7 RIM (3)
(Internal Objects)
HL7 Internal Objects
with Unique OIDs
12/44

HL7 Messaging Environment – 4:
Example
<affectedPerson
codeSystem="2.5.1.76.1.1">
<name use="L">
<first>Sean</first>
<last>Murphy</last>
</name>
</ affectedPerson>
<identifiedPerson
codeSystem=" 2.5.1.44.2.1 ">
<name use="L">
<given>Sean</given>
<family>Murphy</family>
</name>
</identifiedPerson>
HL7 v3
UML
XSD
XML
<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>
HL7 v2
XSD
XML
Context Hospital A:
Patient.hasMedication (Insulin->intersection(Avandia))=isEmpty()
Drug
Policy
131/34/451

Ontologising Health Level Seven Standard (HL7)

Ontology Building Methodologies
 Features Indentified
 Reusability of non-ontological structured resources
 Layering of ontologies
 Local adaptation of ontologies
Enterprise
Ontology
METHONTOLOGY On-To -Knowledge DILIGENT
Reusability +/- +/- +/- +/-
Layering - - - +/-
Local Adaptation - - - +
151/54/451

PPEPR Methodology
Methodological
DILIGENT
PPEPR Methodology
METHONTOLOGY
Enterprise Ontology
On-To-Knowledge
Empirical
Domain Experiences
Road Maps
161/64/451

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
171/74/451

Modelling: Lifting HL7 Resources
Language 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.)
181/84/451

Transformation Rules
 MIF2OWL
 XSD2OWL
element|attribute attribute
element@substitutionGroup
element@type type
complexType|group|attributeGroup class | containedClass
maximumMultiplicity|
minimumMultiplicity
@maxOccurs
@minOccurs
childClass
extension@base|restriction@base
union@memberTypes
attribute@classCode type=Class
HL7 MIF
StaticModel.association|
StaticModel.attribute
Annotation@appinfo
hl7:LongName|hl7:Type
otherAnnotation | appInfo
OWL
ObjectProperty|DataProperty
SubPropertyOf
Range
Class
SubClassOf
max|min
Annotations@label|comment
191/94/451

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: ActObservation
Class: ActClinicalTrial SubClassOf: ActObservation
Class: ActSpecimenObservation SubClassOf: ActObservation
Class: ActGenomicObservation SubClassOf: ActObservation
202/04/451

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: EntityPerson
ObjectProperty: id Domain: Person Range: II
ObjectProperty: name Domain: Person Range: EN
ObjectProperty: administrativeGenderCode Domain: Person Range: CE
ObjectProperty: birthTime Domain: Person Range: TS
ObjectProperty: addr Domain: Person Range: AD
212/14/451

Layering of Ontologies
Top-down Bottom-up
×
Local
Ontology
Local
Ontology
Global
Ontology
(HL7 V2)
Global
Ontology
(HL7 V3)
×
HL7 V2
(coreSchemas)
HL7 V3
(coreSchemas) (1) Datatype
(2) Vocabulary
(common for all hospitals)
Message
Ontology
+ +
Message
Ontology
Message
Ontology
Message
Ontology
HL7 V2 XSD(1) HL7 V2 XSD(2) HL7 V3 XSD(1) HL7 V3 XSD(2)
Local Alignment
Merging
Message Schema
(hospital-specific)
Lifting
Global Alignment
Lifting
22/44

Local Ontology: Merging Local Ontologies
Class: ObservationRequest
SubClassOf: ActObservation
Class: SpecimenObservation
Class: Observer SubClassOf: RoleClass
Class: DiabeticType2Observation
SubClassOf: SpecimenObservation
Class: ObservationOrder.POOB_MT210000UV
Class: Observer.POOB_MT210000UV
SubClassOf: RoleClass
Class: HemoglobinObservation.POOB_MT210000UV
=
=
⊑
Class: ObservationRequest SubClassOf: ActObservation
+
Class: SpecimenObservation SubClassOf: ActObservation
Class: HemoglobinObservation.POOB_MT210000UV SubClassOf: ActObservation
Class: DiabeticType2Observation SubClassOf: SpecimenObservation HemoglobinObservation.POOB_MT210000UV
232/34/451

Alignment: HL7 Global and Local Ontologies
HL7 v3
HL7 v2
GLOBAL LOCAL
PID
PDI
XAD
XON
PID.5
XPN.1
XPN.2
Person
Role
Ad
Organisation
FirstName
classCode
Uni. Hospital
Name
LabTestOrder
Id
Pub. Hospital
Name
OBX1.2
identification
GLOBAL LOCAL
First Name LabTestOrder
25/44

Alignment: Example
Version 3 Version 3
Class: ObservationRequest SubClassOf: ActObservation
Class: SpecimenObservation SubClassOf: ActObservation
Class: DiabeticType2Observation
SubClassOf: SpecimenObservation
Class: ObservationOrder.POOB_MT210000UV
Class: Observer.POOB_MT210000UV SubClassOf: RoleClass
Class: HemoglobinObservation.POOB_MT210000UV
Version 3 Version 2
Class: AD
ObjectProperty: AD.1 Domain: AD Range: AD.1.CONTENT
Class: AD SubClassOf: ANY
ObjectProperty: streetAddressLine Domain: AD Range: Adxp.country
ObjectProperty: state Domain: AD Range: Adxp.state
ObjectProperty: 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>
HL7 Annotation
262/64/451

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)
0.1-0.4
0.4-0.7
0.7-1
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)
0.1-0.4
0.4-0.7
0.7-1
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)
0.1-0.4
0.4-0.7
0.7-1
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)
0.1-0.4
0.4-0.7
0.7-1
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)
0.1-0.4
0.4-0.7
0.7-1
27/44

Alignment: SPARQL Recipes
Class Matching
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'')}}
Property Matching
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 };
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)
0.1-0.4
0.4-0.7
0.7-1
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)
0.1-0.4
0.4-0.7
0.7-1
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)
0.1-0.4
0.4-0.7
0.7-1
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)
0.1-0.4
0.4-0.7
0.7-1
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)
0.1-0.4
0.4-0.7
0.7-1
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

Context, Modularity and Local Policies

Example Scenario
PPEPR
Hospital Drug Policy
Messages
Inconsistency
Observation Order Fulfilment Request
Observation Order Fulfilment Request Acknowledgement
Observation Promise Confirmation
Observation Order Complete (Test Results)
Class: rxnorm:Avandia
SubClassOf: galen:Drug
Class: rxnorm:Insulin
EquivalentProperties:
HospitalA:hasMedication
HospitalB:EHR (Hospital hasTreatment
B)
1
1
2
2
3
3
1
2
3
4
4
5
5
4
5
4
5
EHR (Hospital A)
EHR (General
Practitioner)
DisjointClasses:
HospitalA:hasMedication some rxnorm:Avandia
HospitalA:hasMedication some rxnorm:Insulin
Sean HospitalA:hasMedication rxnorm:Insulin
Sean HospitalB:hasTreatment rxnorm:Avandia
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
NONE OF THEM ADDRESSES ALL FEATURES
 Repairing and Reasoning with Inconsistencies (DeLP)
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:RolePatient
SubClassOf: rim:Role
Class: HA:IrishPPSId
THA THB
SubClassOf: rim:EntityIdentification
Class: HA:LabTestOrder
SubClassOf: rim:Act
Class: HA:HemoglobinTest
SubClassOf: rim:Act
Class: galen:Patient
SubClassOf: galen:Human
Class: HB:OrderLabObservation
SubClassOf: galen:OrderAct
ObjectProperty: HB:hasTreatment
DisjointClasses:
HA:hasMedication some rxnorm:Avandia
HA:hasMedication some rxnorm:Insulin
=
=
=
353/54/451

Distributed Description Logic (DDL)
Oi=〈Ti,Ai, rij〉 {0i= ontology, Ti=Tbox, Ai=Abox, rij = Bridge Rules}
THA THB
=
SubClassOf: rim:Act
SubClassOf: rim:Act
DisjointClasses:
=
=
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)
363/64/451

Packet Description Logic (PDL)
Oi=〈Ti,Ai〉 {0i= ontology, Ti=Tbox, Ai=Abox}
THA THB
=
SubClassOf: rim:Act
SubClassOf: rim:Act
DisjointClasses:
=
=
Class: ( HA:HemoglobinTest and (rim:measures some loinc: 4545-4) )
EquivalentTo: ( galen:BloodSugarTest and (HB:hasCode some snomed: 43396009) )
373/74/451

Database Style-IC
O=〈Tn, TC, A〉 {0= ontology, Tn =Normal Tbox,TC = Constraint Tbox, A=Abox}
SubClassOf: rim:Act
Class: HB:HemoglobinTest
SubClassOf: rim:Act
DisjointClasses:
Tn(HA)
=
=
=
Tn(HB)
TC(HA)
383/84/451

Feature Comparisons
Context-awareness
Modularity Profile & Policy
Management
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

Envisioned Situation
- Context & Policy aware ontological model and reasoning
GALEN SNOMED RIM
Global (D)
Policy1 Policy2 Policy3 Policyn
Local (P)
GALEN SNOMED RIM
Local (P) Global (D)
Hospital A Hospital B
Policy1 Policy2 Policy3 Policyn
414/14/451

Summary
 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
424/24/451

Things cooking at the moment !
HL7 FHIR - OWL HL7 FHIR - RDF
http://www.hl7.org/implement/standards/fhir/
43/44

Thank you
Dr. Ratnesh Sahay
Semantics in e-Health and Life Sciences (SeLS)
Insight Centre for Data Analytics
NUI Galway, The DERI building
IDA Business Park, Lower Dangan
Galway, IRELAND
Tel: + 353 91 495253
Fax: + 353 91 495541
Web: http://www.ratneshsahay.org/
44/44

Ontologising the Health Level Seven (HL7) Standard

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