ontologising the health level seven (hl7) standard · ontologising the health level seven (hl7)...

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

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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

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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.

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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)

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Healthcare Interoperability: Current Situation

EmergencyOncology

Radiology

Laboratory

N*(N-1) Interfaces/Alignments

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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

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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

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Health Level Seven Standard (HL7)

<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>

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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)


/>

XSD (V3)


/>

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

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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)

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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

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Ontologising Health Level Seven Standard (HL7)

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 ‐ ‐ ‐ +

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PPEPR Methodology

PPEPR Methodology

Methodological

Enterprise Ontology

METHONTOLOGY

On‐To‐Knowledge

DILIGENT

Empirical

Road Maps

Domain Experiences

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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

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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.)

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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

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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

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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

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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

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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

=

=

⊑

+

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Aligning HL7 ontologies

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

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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

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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

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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

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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

Context, Modularity and Local Policies

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

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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

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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

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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

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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



THA THB=

=

=

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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)









THA THB=

=

=

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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) )









THA THB=

=

=

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Database Style-IC

O=⟨Tn, TC, A⟩ {0= ontology, Tn =Normal Tbox,TC = Constraint Tbox, A=Abox}




Class: HB:HemoglobinTestSubClassOf: rim:Act





Tn(HA)=

=

=

Tn(HB)

TC(HA)

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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 ‐ ‐ ‐/+

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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

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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

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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

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Things cooking at the moment !

HL7 FHIR - OWL HL7 FHIR - RDF

http://www.hl7.org/implement/standards/fhir/

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Thank you

Dr. Ratnesh SahaySemantics in e‐Health and Life Sciences (SeLS)

Insight Centre for Data AnalyticsNUI Galway, The DERI building

IDA Business Park, Lower DanganGalway, IRELAND

Tel: + 353 91 495253Fax: + 353 91 495541

Web: http://www.ratneshsahay.org/

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