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Daniel Schober on behalf of DebugIT Community

Daniel Schober on behalf of DebugIT Community. Semantic integration of antibiotics resistance patterns. Healthcare Context. A need for ‚IT-biotics‘. DebugIT D etecting and E liminating B acteria U sin G I nformation T echnology

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Daniel Schober on behalf of DebugIT Community

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  1. Daniel Schober on behalf of DebugIT Community Semantic integration of antibiotics resistance patterns

  2. Healthcare Context

  3. A need for ‚IT-biotics‘ DebugIT Detecting and Eliminating Bacteria UsinGInformation Technology Using ‘semantic linked data’ to exploit distributed clinical data Acquire new knowledge Through advanced data mining Apply knowledge in decision support E.g. prescription choice Apply knowledge in monitoring Analyze current & predict future trends Discover patient safety patterns

  4. Using Ontologies in DebugIT • Provide common semantic identifiers • Allow crosstalk within Interoperability Platform • SPARQL query to express research question • Provide formal meaning exploitable by logical & rule-based reasoners • Integrate access to heterogeneous CIS • Normalization via terminologies and textmining

  5. Data normalisation & ontology mapping (annotation) Raw clinical data • different encodings • different languages Ontologies Text Mining De-identification Refined clinical data • uniform format & semantics • anonymized

  6. Data integration architecture • ETL (2) populates local RDTBs in DMZ layer • D2R conversion (3) allows SPARQL integration (4) via Ontologies (DCO, OO)

  7. Linking data values to ontologies (via CVs) Textmining links CIS data values to CVs Create SKOS mappings from CV to Ontology (DCO) SNOMED CT findings Diseases Uniprot NEWT taxonomy  Bacteria WHO ATC codes  Drugs, antibiotics Foundational Model of Anatomy  Human anatomy …  …

  8. Ontology Layers within DebugIT 1 DebugIT Core Ontology (DCO) Clinical domain of infectious diseases OWL-DL 30 Operational ontologies (OO) Implementation, module crosstalk, data mining query building, statistics, analysis, evidences, maths, units, … OWL-Full 7 Data Definition Ontologies (DDO) Describing hospital specific CIS Data model

  9. ‚female patient‘ in different ontology layers Describing real world (independent of data) Describing data

  10. Steps for solving a clinical analysis question Clinician states clinical analysis question in natural language • Clinical Researcher • clinical analysis query via QueryBuilder & SPARQL OOs & DCO • Data Miners • data set queries for each targeted CDR viaSPARQL DDOs • Data Manager • maintains N3 rule set to convert instances from the endpoint specific DDO to OO & DCO • Data Miners • aggregate data set SPARQL result graphs in DCO using the needed conversion rule sets • performs clinical analysis, e.g. using/creating N3 rules using OOs, DCO • formalizes the clinical analysis result, using OOs & DCO • Clinical Researcher • validates result & presents it to Clinician who validates result.

  11. Clinical Analysis SPARQL Query (construct) “What percentage of Escherichia coli cases, cultured from urine samples, is resistant to the combination of trimethoprim/sulfametoxazol (TMP/SMX) or trimethoprim in the period 2006-2010?” CONSTRUCT { ?percentage quex:percentageOf ?total; quex:percentageThat ?part; quex:hasValue ?percentageValue; quex:hasUnit units:percent. ?total rdfs:subClassOf cao:EColi, [ a owl:Restriction; owl:onProperty cao:culturedFrom; owl:someValuesFrom [ rdfs:subClassOf dco:UrineSample; a owl:Restriction; owl:onProperty biotop:outcomeOf; owl:someValuesFrom [ rdfs:subClassOf dco:UrineSampleCollection; a owl:Restriction; owl:onProperty event:during; owl:hasValue [ dco:hasStartDateTime "2006-01-01T00:00:00"^^xsd:dataTime; dco:hasEndDateTime "2010-12-31T23:59:59"^^xsd:dataTime]]]]. ?part rdfs:subClassOf ?total, [ a owl:Restriction; owl:onProperty cao:resistantTo; owl:someValuesFrom [ owl:unionOf (dco:Trimethoprim dco:SulfamethoxazoleAndTrimethoprim)]]}

  12. Clinical Analysis SPARQL Query (where) WHERE { ?percentage quex:percentageOf ?total; quex:percentageThat ?part; quex:hasValue ?percentageValue; quex:hasUnit units:percent. ?total rdfs:subClassOf cao:EColi, [ a owl:Restriction; owl:onProperty cao:culturedFrom; owl:someValuesFrom [ rdfs:subClassOf dco:UrineSample; a owl:Restriction; owl:onProperty biotop:outcomeOf; owl:someValuesFrom [ rdfs:subClassOf dco:UrineSampleCollection; a owl:Restriction; owl:onProperty event:during; owl:hasValue [ dco:hasStartDateTime "2006-01-01T00:00:00"^^xsd:dataTime; dco:hasEndDateTime "2010-12-31T23:59:59"^^xsd:dataTime]]]]. ?part rdfs:subClassOf ?total, [ a owl:Restriction; owl:onProperty cao:resistantTo; owl:someValuesFrom [ owl:unionOf (dco:Trimethoprim dco:SulfamethoxazoleAndTrimethoprim)]]}

  13. Data set SPARQL query (for HUG-DDO) CONSTRUCT { ?antibiogram a ddo:Antibiogram; ddo:hasCulture ?culturing; ddo:hasIdentifiedBacterium [ddo:hasBacteriumCode "562"^^biosko:uniProtTaxonomyDT]; ddo:hasTestedDrug [ddo:hasDrugCode ?atc]; ddo:hasOutcome ?antibiogramResult. ?culturing ddo:hasSampleType ?sampleType; ddo:hasResultDate ?resultDate} WHERE { ?antibiogram a ddo:Antibiogram; ddo:hasCulture ?culturing; ddo:hasIdentifiedBacterium [ddo:hasBacteriumCode "562"^^biosko:uniProtTaxonomyDT]; ddo:hasTestedDrug [ddo:hasDrugCode ?atc]; ddo:hasOutcome ?antibiogramResult. ?culturing ddo:hasSampleType ?sampleType; ddo:hasResultDate ?resultDate. FILTER (?atc = "J01EA01"^^clisko:atc20090101DT || ?atc = "J01EE01"^^clisko:atc20090101DT) FILTER ("2006-01-01T00:00:00"^^xsd:dateTime < ?resultDate && ?resultDate < "2010-12-31T23:59:59"^^xsd:dateTime) FILTER (?sampleType = "102866000"^^clisko:sct20080731DT)} # to be changed to 122575003 for "Urine specimen"

  14. DDO to DCO mapping via N3 rules MAPPING FROM HUG-ddo:Culture TO dco:BacterialCultureProcedure { ?culturing ddo:hasSampleType ?sample. ?Sample skos:exactMatch [skos:notation ?sample]} => { ?culturing biotop:precededBy [a dco:SampleCollection; biotop:hasOutcome [a ?Sample]]}.

  15. Cross-site integrated SPARQL result 2 instances of total result set of 1764 <https://babar.unige.ch:8443/cdr/resource/Culture/100320> a dco:AntimicrobialSusceptibilityTest, dco:BacterialAntibiogramAnalysis, dco:BacterialCultureProcedure; :hasOutcome [:encodes [:qualityLocated [a :SpeciesEscherichiaColiValueRegion]]], [ :encodes [:qualityLocated [a dco:Sensitive]]]; :hasParticipant [a dco:SulfamethoxazoleAndTrimethoprim]; dco:hasResultDateTime "2006-11-03T09:57:00"^^xsd:dateTime. <https://lincoln.imt.liu.se:8443/d2r-server/resource/culture/7219> a dco:AntimicrobialSusceptibilityTest, dco:BacterialAntibiogramAnalysis, dco:BacterialCultureProcedure; :hasOutcome [:encodes [:qualityLocated [a :SpeciesEscherichiaColiValueRegion]]], [ :encodes [:qualityLocated [a dco:Sensitive]]]; :hasParticipant [a dco:Trimethoprim ]; :precededBy [a dco:SampleCollection; :hasOutcome "abnormal urine" ]; dco:hasResultDateTime "2008-10-16T00:00:00"^^xsd:dateTime . …

  16. DCO design principles • OWL-DL • Reasoner for autoclassification & consistency checks during OE • Reasoner infers multiple parenthood • Reusing BioTop • Ensure a rigid modeling view • Provides reuseable constraints (bridges to all TLO) • Concepts harvested from • Hospital CDR schemata • Competency questions from clinical use case • Datadriven bottom up • Domain terminologies in use • Via UMLS or OLS • Ontology modularisation tools (A.Rector) • HL7 v3 based

  17. DCO content (statistics)

  18. A tripartite granular disease model (SDP pattern)

  19. Inference of new facts(BloodSample is a BodyLiquidSample) • BloodSample = Stated Facts • BodyLiquid = • BodyLiquidSample = • Inferred Hierarchy (more structure) • Asserted Hierarchy (flat list) Logics Reasoner

  20. Use CNL for Ontology Evaluation

  21. Next steps Enhance coverage Refinement of DCO structure Addressing drugs dosages & disease therapies Use rectors Snomed CT modularisation algorithm to extract relevant SNOMED CT IDs form DCO-provided seed list Publish and distribute E.g. on Bioportal

  22. DCO evaluation Ultimate overall evaluation Can clinicians run the overall system ? build queries and understand results ? Can data miner create data set results ? do data mining and formalize quality criteria for results ? DCO internal evaluation Fitness for usetested by ability to answer CQ Evaluate validity of assertions by Reasoners Graphical and textual representations to domain experts Serialization of modules into Constrained Natural Languages (CNL)

  23. (Preliminary) Conclusion • Semantically rich application ontologies • Successive Query formalisations are complex … but approach scales over space & time • Used in practice • Practical SPARQL query building • Data integration across 7 EU Hospitals • DL-reasoning helps ontology engineering • DL limitation justified for smaller ontologies • For larger models use rule-based reasoning • As data is dirty we need we need to cope with errors arising

  24. Resources & Acknowledgements Resources • DebugIT project • http://www.DebugIT.eu • Ontology sources • http://purl.org/imbi/dco/dco • TermBrowser • http://www.imbi.uni-freiburg.de/~schober/dco_owlDoc/ Acknowledgements • Hans Cools, Martin Boeker, Kristof Depraetere, Douglas Teodoro, Remy Choquet, Stefan Schulz, Ilinca Tudose, Maren Kechel, Giovanni Mels, Dirk Coalert, Dimitris Iakovidis, the DebugIT team • Funded by grant agreement ICT-2007.5.2-217139

  25. In the Hospital kitchen I was approached by a member of the feared ‘Antibiotics Resistance’ …

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