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Royal College of Surgeons in Ireland Coláiste Ríoga na Máinleá in Éirinn Knowledge representation in TRANSFoRm AMIA CDSS workshop, 24th 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 PHS / Department of General Practice The TRANSFoRm Project PHS / Department of General Practice TRANSFoRm Services 1 CPR Repository Clinical Prediction Rules Service 2 Distributed GP EHRs With CDSS 3 Research Study Designer Study Criteria Design CP Rules Manager CP Classifier 5 CPR Data Mining and Analysis CPR Analysis & Extraction Tool 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 PHS / Department of General Practice 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 PHS / Department of General Practice Ontology Data Representation • Generic model representation constructs and rule formulation – – – – • Data instance representation – – – – • 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) 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 PHS / Department of General Practice Example Question: Provide all differential diagnoses relating to a reason for encounter ICPC2 code “D01” (abdominal pain /cramps general) SELECT ?anyDifferentialDiagnosis WHERE {?anyRFE ?anyDifferentialDiagnosis hasICPC2Code isDifferentialDiagnosisOf "D01"^^xsd:string . ?anyRFE .} EctopicPregnancy Pyelonephritis UrinaryTractInfection ChronsDisease Appendicitis BowelCancer IrritableBowelSyndrome BacterialEnteritis PHS / Department of General Practice Example Question: Give me all rule criteria and cues for all elements of the Little Symptom Rule for UTI SELECT ?anyCriteriaElement WHERE {?anyRuleElement ?anyCriteriaElement ?anyCueElement ?anyCriteriaElement ?anyProperty UTI1Crit1 UrineCloudiness isPresent 1 (True) ?anyCueElement isRuleElementOf isCriteriaOf ? isCueElementOf ?anyProperty rdf:type UTI1Crit2 UrineSmell isPresent 1 (True) ?anyProperty ?anyValue LittleSymptomRule . ?anyRuleElement. ?anyRuleElement. ?anyValue. owl:DatatypeProperty. } UTI1Crit3 Dysuria isPresent 1 (True) UTICrit4 Nocturia isPresent 1 (True) PHS / Department of General Practice WP4 – Technical Architecture Diagram EHR Client Evidence Update via Research Tools WP5 Dynamic Interface Linked to EHR WT 4.5 Data Mining Process Clinical Evidence Web Client Cached Client Data Repository Management Tools HTTP HTTP WP4 Web Services WP4 Client Interfaces Clinical Evidence Web Service (SOAP/WSDL) SPRING MVC Framework TRANSFORM Provenance Service HTTP Evidence Update Service (SOAP/WSDL) HTTP HTTP JAVA Business Objects Sesame RDF API Object / RDF Interface TRANSFORM Security Framework HTTP HIBERNATE Relational to Object Mapping TRANSFORM Vocabulary Service Application Layer Objects TRANSFORM Shared Services SQL Queries Clinical Evidence Repository My SQL Database SPARQL Queries Sesame RDF Repository My SQL Database RDF/XML OWL/XML Protégé Ontology Development 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 PHS / Department of General Practice 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! PHS / Department of General Practice