Royal College of Surgeons in Ireland

draughtplumpInternet and Web Development

Oct 22, 2013 (3 years and 8 months ago)

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PHS / Department of General Practice

Royal College of Surgeons in Ireland

Coláiste Ríoga na Máinleá in Éirinn

Knowledge representation in TRANSFoRm

AMIA CDSS workshop, 24
th

October 2011

Derek Corrigan, Borislav Dimitrov, Tom Fahey

PHS / Department of General Practice

Overview


Aim to provide overview of TRANSFORM approach to knowledge
representation


provide discussion points



Distinguish between clinical knowledge vs. patient data



Description of development of ontology to support clinical evidence



Examples of how the ontology can be used to support querying data



Discuss the benefits of this approach



Discuss the challenges and issues encountered using this approach

PHS / Department of General Practice

Clinical knowledge


what do we mean?


Patient Data


traditional model focus


Documentation to support record of patient encounter


Tends to be historic and static to a point in time in nature


Data or document presentation focussed


Existing clinical models traditionally have been EHR focussed



Clinical Knowledge


Clinical facts derived from research data that stands alone and
separate from a patient context


Dynamically changing as research evolves and develops


Rule based to implement forms of reasoning



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The TRANSFoRm Project

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1 CPR Repository

Clinical Prediction
Rules Service

TRANSFoRm

Services


5 CPR Data Mining

and Analysis


3 Research Study Designer


2 Distributed

GP EHRs

With CDSS

CPR Analysis &
Extraction Tool

CP Classifier

CP Rules
Manager

Study Criteria
Design



Find Eligible Patient




4 Research Study Management


Recruit Eligible Patient

Study Data Management

PHS / Department of General Practice

TRANSFoRm approach


Clinical Prediction Rule


core model structure


Well defined and has underlying statistical model in the form of
logistic regression models to support electronic derivation from
research data


TRANSFoRm has potential to address limitations of traditional CPR
development


large populations for derivation, validation
infrastructure, dissemination of CPRs as guidelines



Ontology of clinical evidence


Using Protégé to define an ontology of clinical evidence that
implements CPRs as an evidence interpretation mechanism

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Ontology Development Tools


Protégé


Ontology Development



Sesame Triple Store


provides persistent representation




Sesame API


provides for programmatic update/manipulation



Ontology will provide a service oriented semantic contract for the
representation of clinical evidence knowledge for other TRANSFoRm
services and software artifacts e.g. provenance, data mining, CDSS
interface



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Ontology Data Representation


Generic model representation constructs and rule formulation


RDFS (Schema language) and Web ontology language (OWL)


E.g. “EvidenceSymptom”


“isSymptomOf”


“EvidenceDiagnosis”


SWRL (Semantic Web Rule Language) allows definition of complex chained rules


Person (?x1) ^ hasSibling(?x1,?x2) ^ Man(?x2)


hasBrother(?x1,?x2)



Data instance representation


Resource Description Format triples (RDF)


“Subject


Predicate


Object”


E.g. “Dysuria”


“isSymptomOf”


“UrinaryTractInfection”


Predicates/relationships are directional in nature


E.g. “UrinaryTractInfection”


“hasSymptom”


“Dysuria”




Distribution format


supports concept composition


Tagged text file in XML like syntax for easy distribution


Import and reuse other ontologies as building blocks

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Example Question: Provide all differential
diagnoses relating to a reason for
encounter ICPC2 code “D01” (abdominal
pain /cramps general)


SELECT ?anyDifferentialDiagnosis



WHERE

{?anyRFE


hasICPC2Code



"D01"^^xsd:string .


?anyDifferentialDiagnosis


isDifferentialDiagnosisOf

?anyRFE .}



EctopicPregnancy

Pyelonephritis UrinaryTractInfection

ChronsDisease

Appendicitis

BowelCancer

IrritableBowelSyndrome

BacterialEnteritis


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Example Question: Give me all rule criteria
and cues for all elements of the Little
Symptom Rule for UTI

SELECT ?anyCriteriaElement ?anyCueElement ?anyProperty ?anyValue


WHERE

{?anyRuleElement


isRuleElementOf


LittleSymptomRule .

?anyCriteriaElement


isCriteriaOf ?


?anyRuleElement.

?anyCueElement


isCueElementOf


?anyRuleElement.

?anyCriteriaElement


?anyProperty


?anyValue.

?anyProperty


rdf:type



owl:DatatypeProperty. }



UTI1Crit1


UTI1Crit2


UTI1Crit3


UTICrit4

UrineCloudiness

UrineSmell


Dysuria


Nocturia


isPresent


isPresent



isPresent



isPresent


1 (True)



1 (True)



1 (True)



1 (True)




PHS / Department of General Practice

Observations on the ontological approach


RDF provides an alternative model approach by reducing data representation to
a very simple form without use of complex reference models


reduced
complexity paradoxically increases power!



The addition of RDFS and OWL add a semantic interpretation layer on top of
the data representation that supports composition and merging of diverse data
sources and subsequent inference to generate new facts



SPARQL allows for very complex querying using compact data representation
that can be easily be done in ‘two directions’ to support ‘top
-
down’ analysis or
‘bottom
-
up’ analysis


works well for diagnostic view of data



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Challenges of ontological approach


Ontology validation


who arbitrates on the clinical accuracy and completeness
of models? Knowledge governance Vs. Standards governance



An ontology is not a working application


development tools are not
application focussed and needs ontological to relational mapping to support
integration with relational data to provide the ‘working’ application context


duplication of effort?



Ontology maintenance is intensive


tools still immature/poorly integrated in
development environments



Integration /interoperability with EHR using standards and clinical vocabularies


granularity/mapping issues e.g. ICPC2




PHS / Department of General Practice

Thank You


Discuss!