Artikel
Layered ontology-facilitated clinical data integration within the DebugIT EU project
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Veröffentlicht: | 20. September 2011 |
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Gliederung
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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
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- 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.
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- 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.