An ontological approach for the exploitation of clinical data

Transcription

1 An ontological approach for the exploitation of clinical data Ariane Assélé Kama 1,Rémy Choquet 1, Giovanni Mels 2, Christel Daniel 1,3, Jean Charlet 1,3, Marie-Christine Jaulent 1 1 INSERM, UMR_S 872, Eq. 20, Université Pierre et Marie Curie, Paris, France; 2 AGFA HealthCare NV, Moutstraat 100, 9000 Gent, Belgium; 3 APHP, Assistance Publique des Hôpitaux de Paris, Paris, France; MEDINFO Copenhagen, 22 th of August 2013 Oral Presentation Presenter: Ariane Assélé Kama 1 Assises GDR I3 Strasbourg 01/07/2010

2 DebugIT Detecting and Eliminating Bacteria Using Information Technology European project FP7 (grant #217139) with 14 partners. Aggregate data stored in European hospitals Build a unified system Infectious disease control Antimicrobial resistances Disclaimer: this presentation reflects solely the views of the authors and no guarantee or warranty is given that it is fit for any particular purpose. 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 Assises GDR I3 Strasbourg Slide 201/07/2010

3 Hospital Information Systems Introduction Clinical Databases Consolidate, archive Large data streams ETL Process Extract Transform Load Diagnostic or Therapeutic decisions Clinical or Epidemiological research Analysis Space Decision making Data analysis Data mining 3 Assises GDR I3 Strasbourg Slide 301/07/2010

4 Hospital Information Systems Why we need to use semantics resources? Data Sources ID Bacterium Antibiotic Result Value 1 E. Coli Amoxicilline Resistant 25 2 Escherichia coli Cefoperazone Sensitive 28 3 Pseudomonas aeruginosa Ofloxacine Intermediate 10 Question ID Bactérie Antibiotique R V 10 Escherichia.Coli Cefpirome R Hafnia alvei Quinolones S Pseudomonas aeruginosa Ofloxacine I 10 What are the results of susceptibility testing of E. Coli resistant to B-lacatam? Problem " Lack of knowledge : Amoxicillin is a B-lactam " E. Coli and Escherichia coli refer to the same bacteria " Fields bacterium & bactérie refer to the same bacteria concept Hypothesis Using an ontology to enrich clinical data exploitation Objective To exploit domain knowledge throughout ontology, as a user oriented view, to query clinical data, building a SPARQL Endpoint. 4 Assises GDR I3 Strasbourg Slide 401/07/2010

5 Material & Methods SPARQL Endpoint Building Process (a) Traditionnal multidimentionnal datawarehouse 2 D2R Server / Joseki - Mapping File: (concepts) ó (tables/colums) - RDF Graphe (fichier.n3) - Data mapping 3 SPARQL Endpoint - Clinical Data access - SPARQL queries 1 Ressources - Clinical Database (HEGP) - DCO 7 : Ontologie (DebugIT) (b) SPARQL Endpoint building approach 5 Assises GDR I3 Strasbourg Slide 501/07/2010

6 SPARQL Endpoint building Process Database mapping file DebugIT Core Ontology (DCO) Database Information Model 6 Assises GDR I3 Strasbourg Slide 601/07/2010 MEDINFO Copenhague 22th of August 2013

7 SPARQL Endpoint building Process Database mapping to DCO Ontology Data Concepts 7 Assises GDR I3 Strasbourg Slide 701/07/2010

8 SPARQL Query Example Illustration of a SPARQL Query building PREFIX biotop : < PREFIX dco: < PREFIX inserm: < SELECT DISTINCT * WHERE { GRAPH < {?antibiotic1 a dco:betalactamantibiotic.?bacteria a biotop:speciesecherichiacoliregion. } GRAPH < {?susceptibility a inserm:resultantibiogram; inserm:antibiogram_id?antibiogram; inserm:antibiotic_tested?antibiotic1; inserm:antibiotic_result?r1. Mapping file between data and ontology We get the corresponding URIs of the B-Lactam antibiotics and E. coli bacteria From the mapping file, specifications data from the database are recovered } }?antibiogram a inserm:antibiogram; inserm:bacteria_analyzed?bacteria. 8 Assises GDR I3 Strasbourg Slide 801/07/2010

9 Results SPARQL Query running Initial data " antibiogram results " Performed on 61 antibiotics, including 22 Beta-Lactam " 165 bacteria SPARQL Query result D2R Server results of susceptibility of resistance of E. Coli to beta-lacatam in s Joseki : using ontology concept results of susceptibility of resistance of E. Coli to beta-lacatam in s 9 Assises GDR I3 Strasbourg Slide 901/07/2010

10 Results Comparison with the HEGP microbiologists reports E. Coli vs CEFIXIM: sensitive at hegp vs debugit (%) HEGP % (Internal report) DebugIT (Sparql Endpoint) % Total ddo:sensitive Rate sensitivity of E. coli to Cefixim at HEGP over a period of 6 years Graphic rate of E. Coli sensitivity to Cefixim at HEGP hospital over a period of 6 years. 10 Assises GDR I3 Strasbourg Slide 101/07/2010

11 Limits and Future Work Conclusion Using semantic resource to exploit clinical data Ontologies are used to enrich the data analysis process We run fewer queries using ontology and retrieve all data Semantic relations is-a hierarchy was used in this study All existing semantic relations could be used (e.g. equivalence) Database mapping to ontology Mapping manually done Link an instance to a concept Perspectives Build a data definition ontology from the database information model Define mapping rules between the domain ontology (DCO) an the data definition 11 Assises GDR I3 Strasbourg Slide 11 01/07/2010

12 The DebugIT European Projet Conclusion The Semantic Interoperability Platform 12 Assises GDR I3 Strasbourg Slide 1201/07/2010

13 Acknowledgments DebugIT, EU-IST-FP EAGL, SNF Agfa Healthcare Giovanni Mels Hans Cools INSERM EQ20 UMRS 872 DebugIT partners Agfa Healthcare (Belgium) Empirica (Germany) Gama Sofia Ltd (Bulgaria) INSERM (France) IZIP (Czech Republic) Linköping University (Sweden) TEILAM (Greece) University College London (UK) HUG (Switzerland) Freiburg University (Germany) Geneva University (Switzerland) Averbis (Germany) MDA (Czech Republic) HES-SO (Switzerland) 13 Assises GDR I3 Strasbourg Slide 1301/07/2010

14 Thank you for your attention 14 Assises GDR I3 Strasbourg Slide 1401/07/2010