gms | German Medical Science

MAINZ//2011: 56. GMDS-Jahrestagung und 6. DGEpi-Jahrestagung

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V.
Deutsche Gesellschaft für Epidemiologie e. V.

26. - 29.09.2011 in Mainz

Layered ontology-facilitated clinical data integration within the DebugIT EU project

Meeting Abstract

  • Daniel Schober - Institut für Medizinische Biometrie und Medizinische Informatik, IMBI, Freiburg i Br.
  • Hans Cools - Advanced Clinical Applications Research Group, Agfa HealthCare NV, Moutstraat 100, Gent, Belgium
  • Remy Choquet - INSERM UMRS872 EQ20, 15 rue de l'ecole de medecine, 75006, Paris, France
  • Giovanni Mels - Advanced Clinical Applications Research Group, Agfa HealthCare NV, Moutstraat 100, Gent, Belgium
  • Kristof Depraetere - Advanced Clinical Applications Research Group, Agfa HealthCare NV, Moutstraat 100, Gent, Belgium
  • Martin Boeker - Institut für Medizinische Biometrie und Medizinische Informatik, IMBI, Freiburg i Br.
  • Dirk Colaert - Advanced Clinical Applications Research Group, Agfa HealthCare NV, Moutstraat 100, Gent, Belgium

Mainz//2011. 56. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (gmds), 6. Jahrestagung der Deutschen Gesellschaft für Epidemiologie (DGEpi). Mainz, 26.-29.09.2011. Düsseldorf: German Medical Science GMS Publishing House; 2011. Doc11gmds465

DOI: 10.3205/11gmds465, URN: urn:nbn:de:0183-11gmds4654

Veröffentlicht: 20. September 2011

© 2011 Schober et al.
Dieser Artikel ist ein Open Access-Artikel und steht unter den Creative Commons Lizenzbedingungen (http://creativecommons.org/licenses/by-nc-nd/3.0/deed.de). Er darf vervielfältigt, verbreitet und öffentlich zugänglich gemacht werden, vorausgesetzt dass Autor und Quelle genannt werden.


Gliederung

Text

Introduction: Recent years saw a large increase in bacterial antibiotics-resistance, leading to serious impact on hospital patient safety. The DebugIT (Detecting and Eliminating Bacteria Using Information Technology) project [1] strives to integrate clinical data from seven hospitals across Europe to enable cross-country resistance-data comparison, safety guideline development and to ultimately counteract antibiotics resistance EU-wide applying an ‘ITbiotics’ approach. In order to detect variances in antibiotics prescriptions and compare their impact on resistance development between different hospitals and countries, DebugIT needs to find a way to access heterogeneous clinical data. Ontologies seem a reasonable approach to achieve such integration.

Material and methods: Access to geographically distributed, multilingual and semantically heterogeneous data is integrated via semantic web technologies like OWL ontologies, automatic reasoner and SPARQL endpoints for querying [2]. D2R servers were deployed for real-time transformation of relational data into a semantic web accessible RDF-format, providing SPARQL access by means of a site-specific Data Definition Ontology (DDO), a wrapper layer matching the relational database schemata. DDOs are used in site-specific data set queries and mediate successive further data formalisation steps, i.e. exploiting ontology conversion rules to derive instances of a highly formal DebugIT core ontology (DCO, [3]), serving as universal cross-site integration layer, allowing cross-site data querying and intelligent result analysis. In parallel database values representing standard terminology entries are converted into DCO instances via SKOS terminology mappings.

Results: The overall DebugIT interoperability platform relies on multiple layers of ontologies, conversion rules and terminology mapping files to allow data integration, subsumptive querying and rule-based data mining approaches, enabling health care pattern recognition and decision support on antibiotics prescriptions.

As the semantic gap from the clinical data to formal ontologies, and vice-versa from clinical questions in natural language to DDO SPARQL-queries, is too large to be bridged by a one step ontology integration approach, we enable users to generate queries in a stepwise multi-layered manner, from the informal to formal semantics, relying on a multiplicity of ontology and terminological mapping layers of different ‘user-compliance’ and ontological rigidity [4].

Discussion/conclusion: Data is formalized ontologically and hence can be integratively queried across all sites and languages, rendering results universally re-useable. Although the presented multilayered data integration approach is complex and requires new user soft-skills, we believe it is nevertheless feasible, as it represents the only scalable solution, enabling seamless integration of further new Hospital sites to join the project.


References

1.
Lovis C, et al. DebugIT for patient safety - improving the treatment with antibiotics through multimedia data mining of heterogeneous clinical data. Stud Health Technol Inform. 2008;136:641-6.
2.
Anjum A, Bloodsworth P, Branson A, et al. The Requirements for Ontologies in Medical Data Integration: A Case Study. 11th ACM International Database Engineering and Applications Symposium (IDEAS 2007), pp. 308-314.
3.
Schober D, Boeker M, Bullenkamp J, Huszka C, Depraetere K, Teodoro D, et al. The DebugIT core ontology: semantic integration of antibiotics resistance patterns. Stud Health Technol Inform. 2010;160 (Pt 2):1060-–1064.
4.
Schulz S, Schober D, Daniel C, Jaulent MC. Bridging the semantics gap between terminologies, ontologies, and information models. Stud Health Technol Inform. 2010;160(Pt 2):1000-4. PubMed PMID: 20841834.