Interoperability driven integration of biomedical data sources

Transcription

1 Interoperability driven integration of biomedical data sources Douglas TEODORO a,1, Rémy CHOQUET c, Daniel SCHOBER d, Giovanni MELS e, Emilie PASCHE a, Patrick RUCH b, and Christian LOVIS a a SIMED, University Hospitals of Geneva and b HEG, University of Applied Sciences, Geneva, Switzerland; c INSERM, Université Pierre et Marie Curie, Paris, France; d Freiburg University Medical Center, Germany; e AGFA Healthcare, Ghent, Belgium Oslo, 30 August

2 The DebugIT project Funded by the European Community's Seventh Framework Program under grant agreement n FP (7M ) Project period: from Jan 1st, 2008 to December 31st, 2011 (?) 14 Partners Disclaimer: this presentation reflects solely the views of the DebugIT team. The European Commission, Directorate General Information Society and Media, Brussels is not liable for any use that may be made of the information contained therein 2

3 Aim and objectives Design of a data integration architecture for helping with researching and monitoring of antimicrobial resistance using existing operational microbiology databases Integrate heterogeneous operational clinical information systems Design methods to interoperate with various storage systems, Implement a data source mediator Provide common semantics to the data Formalize data source models and data types Provide ubiquitous access to the data Expose laboratory data from the data sources on the Internet 3

4 The virtual Clinical Data Repository A data integration platform for existing clinical data Primarily focused on antimicrobial data but extensible to other domains Based on Semantic Web technologies Follows the hybrid ontology-driven integration approach Multiple semantically flat data definition ontologies are mapped to a common semantically defined domain ontology Provide three levels of interoperability in the data integration process Technical Syntactic Semantic 4

5 Methods: Technical interoperability An intermediate storage layer was designed to provide a common storage system RDF store RDF store RDMBS Based on RDF store RDF model SPARQL protocol RDMBS ETL RDF store HTTPS Internet HTTPS RDF store ETL jobs provide interface to the different storage systems RDF store RDF store Data sources are connected via HTTPS/SPARQL protocol Text files <XML> files Intranet DMZ Internet Extract-Transform-Load Local security 5

6 Methods: Syntactic interoperability Data cataloging Bottom-up process Local data types are aligned using biomedical terminologies WHO-ATC, SNOMED-CT, NEWT Multi-stage text-based classification are used for automatic normalization Ruch, Bioinformatics 2006; Daumke, GDMS 2010 A domain ontology was designed to represent the field (DebugIT Core Ontology) Terminologies are mapped to DCO using SKOS ontology Disease ICD-10 Laboratory SNOMED CT mapping LOINC DCO (instances) Local concepts Local formal concepts WHO- ATC mapping NEWT DDO instance Drug Bacteria Global concepts Data normalization 6

7 Methods: Semantic interoperability Local RDF data store (local CDR) models are formalized using a semantically flat data definition ontology (DDO) ER mapping DDO DDO EAV/ CR Local models are mapped to their respective DDO DDO mapping DCO (classes and properties) mapping DDO DDOs are mapped to DCO closing the gap between local and domain semantics open- EHR DDO DDO HL7- RIM Local model Local formalized model Shared domain model Data model mapping 7

8 Methods: Query model Domain Query?ab a dco:antibiogram; dco:hasresultdate? date CDR Query?ab a ddo:bacteriologie; ddo:hasdate?date CDR Results fetched and returned in Reasoning Mapping CDR the RDF graph format using local Results terminologies fetched Validation DDO1 and returning in DDO2 RDF graph Aggregation format using DDO3 Reasoning local terminologies DCO 8

9 Results: Pilot network Seven healthcare institutions are sharing antimicrobial resistance data using the framework GAMA (Sofia-BG), HUG (Geneva-CH), INSERM (Paris-FR), IZIP (Prague-CZ), LiU (Linköping-SE), TEILAM (Lamia-GR) and UKLFR (Freiburg-DE) 9

10 Results: Ontology added-value Use of ontology for automatic clustering of antibiograms (e.g. by antibiotic classes) 10

11 Results: Performance evaluation In the preliminary tests, a set of long period queries were performed to evaluate the CDR response time Network is responsible for 41% to 49% of the retrieval time for the sets containing more than 1000 tuples E.g.: What is the evolution of resistance of Klebsiella pneumonia from Jun 2005 to Jun 2009? Source #Tuples retrieved Retrieval time (s) SPARQL Network #Tuples/s GAMA HUG INSERM IZIP LIU TEILAM UKLFR

12 Conclusions Developing a full semantic web-compliant distributed CDR is feasible Seven healthcare institutions compose the demonstration network CDR exposes standardized and formalized microbiology clinical database The query mediation process is limited Logically impossible to map a priori from the global to local ontologies To be usable by end-users (clinical researches, physicians) the system needs to be encapsulated by query templates 12

13 Conclusions In the query plan, most of the data aggregation is done centrally Push reasoning down to local sources to improve network response A production version of the CDR is expected to be available for surveillance systems and clinical research by the end of the year 13

14 The Partners Agfa HealthCare, Belgium empirica Gesellschaft für Kommunikations- und Technologieforschung mbh, Germany Gama Sofia Ltd., Bulgaria Institut National de la Santé et de la Recherche Médicale, France Internetový Pristup Ke Zdravotním Informacím Pacienta (IZIP), Czech Republic Linköpings Universitetet, Sweden Technologiko Expedeftiko Idrima Lamias, Greece University College London, United Kingdom Les Hôpitaux Universitaires de Genève, Switzerland Universitätsklinikum Freiburg, Germany Université de Genève, Switzerland Averbis, Freiburg, Germany MDA, Czech Republic HEG, Geneva, Switzerland 14

15 Methods: Query model CONSTRUCT "?graph WHERE " {?graph a " ddo:concept. }" Aggregat ion Mappin g Retrieving Douglas Teodoro - MIE