daniel schober on behalf of debugit community

IDO-WS 2010 Daniel Schober

Daniel Schober on behalf of DebugIT Community

Semantic integration of antibiotics resistance patterns


Healthcare Context


A need for ‚IT-biotics‘

• DebugIT– Detecting and Eliminating Bacteria UsinG Information

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


The DebugIT project: general architecture

Activities

Repositories

ClinicalData

Repository(aggregated,distributed)

Analysis

KnowledgeRepositories

(aggregated,distributed)

Reasoningengine

Knowledge

ClinicalInformation

System

clinical dataclinical data

Knowledge

1-CollectData

2-Learn

3-StoreKnowledge

4-Apply

Activities

Repositories

ClinicalData

Repository(aggregated,distributed)

Analysis

KnowledgeRepositories

(aggregated,distributed)

Reasoningengine

Knowledge

ClinicalInformation

System

clinical dataclinical data

Knowledge

1-CollectData

2-Learn

3-StoreKnowledge

4-Apply


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


Data normalisation & ontology mapping (annotation)

Refined clinical data– uniform format & semantics– anonymized

Ontologies Text Mining

De-identification

Raw clinical data– different encodings– different languages


Data integration architecture

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


Linking data values to ontologies (via CVs)

1. Textmining links CIS data values to CVs

2. 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

… …


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


Describing data

Describing real world (independent of data)

‚female patient‘ in different ontology layers


Steps for solving a clinical analysis question

Clinician states clinical analysis question in natural language

1. Clinical Researcher– clinical analysis query via QueryBuilder & SPARQL OOs & DCO

2. Data Miners– data set queries for each targeted CDR via SPARQL DDOs

3. Data Manager– maintains N3 rule set to convert instances from the endpoint specific

DDO to OO & DCO

4. 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

5. Clinical Researcher– validates result & presents it to Clinician who validates result.


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)]]}


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)]]}


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"


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]]}.


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 .

…


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


DCO content (statistics)

•Ontology elements & axioms •Overall •DCO •BioTop

•Classes •1311 •1014 •375

•Object Properties (relations) •78 •3 •74

•Datatype Properties •11 •10 •0

•Subclass Axioms •1494 •1050 •444

•Equivalent Class Axioms •197 •98 •99

•Disjoint Axioms •76 •1 •75


A tripartite granular disease model (SDP pattern)


Inference of new facts(BloodSample is a BodyLiquidSample)

Stated Facts

•Inferred Hierarchy (more structure)

Logics Reasoner

•BodyLiquidSample =

•BloodSample =

•Asserted Hierarchy (flat list)

•BodyLiquid =


•Use CNL for Ontology Evaluation


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


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 use tested 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)


(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


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


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

daniel schober on behalf of debugit community

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