Practical experience: FP7 DebugIT project

Transcription

1 Practical experience: Setting up a Clinical Data Repository FP7 DebugIT project Douglas Teodoro On behalf of DebugIT partners Oslo, 29 August 2011

2 Scope The Clinical Data Repository (CDR) components. How to transform operational database into DebugIT compliant RDF endpoint. Lessons learned in the CDR deployment process. Slide 2 Douglas Teodoro DebugIT MIE 11

3 Using formal resources Operational world Step 1 Step 2 Local Formal world Global GUI Operational world SQL DSSQ CASQ query builder DB scheme values conversion result in DDO form conversion result in DO form analysis dashboard CIS mapping DB: database CIS: Clinical Information System DDO: Data Definition Ontology CDR: Clinical Data Repository DDO CDR conversion rules DO: Domain Ontology - e.g. DebugIT Core Ontology DCO OO: Operational Ontology vcdr: virtual Clinical Data Repository - e.g. EM/PR: Electronic Medical/Patient Record CASQ: Clinical Analysis SPARQL Query DSSQ: Data Set SPARQL Query GUI: Graphical User Interface / SQL: Structured Query Language DO vcdr/emr analysis rules - formal data - integrated - analysed for - decision support - workflow At development (deployment) time: creating "formal library" At runtime: formalizing (via querying) and deducting (via reasoning with rules) OO Slide 3 Douglas Teodoro DebugIT MIE 11

4 CDR at a glance Requirements Expose EHR data in the RDF format Communicate in the SPARQL protocol Formalised data model and types <<? >> 1 <<HTTPS/SPARQL>> DSSQ Legacy System 2 DDO Result Graph Table A (Antibiogram) ID Drug " Result AMOX R 2 TMP S 3 PENIC S 3 Normalization Services A#1 hasdrug amoxicillin A#1 hasresult resistant A#2 hasdrug trime A#2 hasresult sensitive A#3 hasdrug penicillin A#3 hasresult sensitive Internet Bus Slide 4 Douglas Teodoro DebugIT MIE 11

5 CDR 1: SPARQL Endpoint Three possible deployment scenarios: 1. RDF View: Direct connection to the legacy EHR system using a RDB-to-RDF engine such as D2R. 2. RDF Mirror: Extract data using ETL jobs to a local datamart and expose using a RDF View on top of the datamart. 3. RDF Native Source: Deploy a RDF storage such as Sesame, Jena TDB or OWLIN and extract the data to it using ETL jobs. Slide 5 Douglas Teodoro DebugIT MIE 11

6 CDR 1: SPARQL Endpoint Deployment scenarios at HUG Scenario 1: RDF View Legacy System Oracle 10.4 Synonyms <<SQL>> D2R Server Scenario 2: RDF Mirror Automatic synchronisation Lecacy DB HL7-like Integrated laboratory database Microbiology period >1989 to present Datamart Star model Specific microbiology results Period >2005 to present Legacy System Oracle 10.4 Synonyms <<Java>> ETL Job Manager Datamart Oracle 10.4 Tables <<SQL>> D2R Server Another system to maintain Improved performance Better production system stability Slide 6 Douglas Teodoro DebugIT MIE 11

7 CDR 2: Data Definition Ontology Role: Formalize data source model Automatically generated from the data Extract from DB schema Or from D2R mapping file At HUG, since we have opted for D2R the DDO was automatically extracted from the RDB mapping generated by D2R DebugIT also provides a tool that generates the DDO independent of D2R: generate-ddo -d [driver.class.name] -u [db-user] -p [db-password] -n [ddo prefixname] b [ddo uri] DDO Slide 7 Douglas Teodoro DebugIT MIE 11

8 DDO generation and validation Generation: generator.py -m mapping.n3 -d ddo.n3 -l fr DDO # MAPPING (Table ANTIBIOTIQUE) map:antibiotique a d2rq:classmap; d2rq:datastorage map:database; d2rq:uripattern d2rq:class ddo:antibiotique; d2rq:classdefinitionlabel ANTIBIOTIQUE";. [] a d2rq:propertybridge; d2rq:belongstoclassmap map:antibiotique; d2rq:property ddo:antibiotique_cd_antib; d2rq:propertydefinitionlabel "ANTIBIOTIQUE CD_ANTIB"; d2rq:column ANTIBIOTIQUE.CD_ANTIB";. [] a d2rq:propertybridge; d2rq:belongstoclassmap map:antibiotique; d2rq:property ddo:antibiotique_lib_antib; d2rq:propertydefinitionlabel "ANTIBIOTIQUE LIB_ANTIB"; d2rq:column ANTIBIOTIQUE.LIB_ANTIB";. # DDO (Class ANTIBIOTIQUE) ddo:antibiotique a rdfs:class; rdfs:label "ANTIBIOTIQUE"@fr; rdfs:isdefinedby ddo:; skos:definition "<ANTIBIOTIQUE_definition>"@fr ; rdfs:subclassof [ a owl:restriction; owl:onproperty ddo:antibiotique_lib_antib ], [ a owl:restriction; owl:onproperty ddo:antibiotique_cd_antib ]. ddo:antibiotique_lib_antib a owl:datatypeproperty; rdfs:isdefinedby ddo:. ddo:antibiotique_cd_antib a owl:datatypeproperty; rdfs:isdefinedby ddo:. Validation: eye nope mapping.n3 ddo.n3 validaterules.n3 query query.n3 Slide 8 Douglas Teodoro DebugIT MIE 11

9 CDR 3: Data normalization Multi staged normalization workflow Input D) Validation and semi-automatic mapping A) Gold mapping (validated mappings) knn text based categorization C) Morpho-semantic Analysis B) Exact terminology mapping (including synonyms, xrefs) Slide 9 Douglas Teodoro DebugIT MIE 11

10 CDR 3: Data normalization DebugIT Catalogue Bottom up ontology building: define a limited set of descriptors Add definitions starting from the content of the clinical data warehouse ATC, NEWT, ICD-10, SNOMED, MeSH Normalization scripts based on xcat (GOCat, Ruch 2006): SNOCat for SNOMED: MeSHCat for MESH: GOCat for Gene Ontology: Perl scripts: longest match first + Levenshtein distances Results > 95% Slide 10 Douglas Teodoro DebugIT MIE 11

11 To finalise Deploy D2R in a web server Set up a HTTPS container Define username and password Make the DDO available in the Web (Scenario 2: Set up a mirror database of your preference (schema and RDBMS) Improve the ETL jobs using Talend for example (or reuse DebugIT ones). Run the ETL jobs using a task manager such as Quartz) Slide 11 Douglas Teodoro DebugIT MIE 11

12 Performance evaluation Clinical question 1: What is the total amount of cultured <bacteria> which were tested against <antibiotic> in the period <fromdate> - <todate>? fromdate = todate = bacteria = Escherichia coli antibiotic = [Amoxicillin, Cefepime, Cefuroxime, Ciprofloxacin, Coamoxiclav, Co-trimoxazole, Gentamicin, Norfloxacin, Piperacillin +Tazobactam, Piperacillin] Clinical question 2: What is the daily amount of cultured <bacteria> which were tested against <antibiotic> in the period <fromdate> - <todate>? fromdate = todate = bacteria = MRSA antibiotic = [Fusidic Acid, Clindamycin, Erythromycin, Flucloxacillin, Fosfomycin, Gentamicin, Penicillin, Rifampicin, Teicoplanin, Tetracycline] Slide 12 Douglas Teodoro DebugIT MIE 11

13 Performance evaluation Query response time RDF View RDF Mirror Query 1 Query 2 Response Time [ms] Query RDF View RDF Mirror Mean (s) Std (s) Mean (s) Std (s) Query Query Response time of RDF Mirror is enough for HUG needs! Slide 13 Douglas Teodoro DebugIT MIE 11

14 Lessons Learned Data normalization was the most expensive task in the CDR deployment D2R handles some queries very poorly SPARQL query filters might be not pushed down to the DB by D2R depending on the query clauses, LET for example Oracle Synonyms are not well handled. During the SQL conversion, a table is joined for every column in the query (even if the columns refers to the same table) RDF Mirror is more flexible in general D2R is limited to RDBMS Even for RDMBS, D2R results for some clinical schema such as EAV might be unexpected RDF Mirror provided better performance but also protected the production system against external component bugs and user mistakes The preliminary efficiency results of the CDR are promising. Slide 14 Douglas Teodoro DebugIT MIE 11

15 Questions?