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A unified structural/terminological interoperability framework based on LexEVS: application to TRANSFoRm.

Ethier JF, Dameron O, Curcin V, McGilchrist MM, Verheij RA, Arvanitis TN, Taweel A, Delaney BC, Burgun A - J Am Med Inform Assoc (2013)

Bottom Line: The GIM was successfully instantiated in TRANSFoRm as the clinical data integration model, and necessary mappings were created to support effective information retrieval for software tools in the project.Information models, terminologies and mappings are all stored in LexEVS and can be accessed in a uniform manner (implementing the HL7 CTS2 service functional model).The system is flexible and should reduce the effort needed from data sources personnel for implementing and managing the integration.

View Article: PubMed Central - PubMed

Affiliation: INSERM UMR936, Université de Rennes 1, Rennes, France.

ABSTRACT

Objective: Biomedical research increasingly relies on the integration of information from multiple heterogeneous data sources. Despite the fact that structural and terminological aspects of interoperability are interdependent and rely on a common set of requirements, current efforts typically address them in isolation. We propose a unified ontology-based knowledge framework to facilitate interoperability between heterogeneous sources, and investigate if using the LexEVS terminology server is a viable implementation method.

Materials and methods: We developed a framework based on an ontology, the general information model (GIM), to unify structural models and terminologies, together with relevant mapping sets. This allowed a uniform access to these resources within LexEVS to facilitate interoperability by various components and data sources from implementing architectures.

Results: Our unified framework has been tested in the context of the EU Framework Program 7 TRANSFoRm project, where it was used to achieve data integration in a retrospective diabetes cohort study. The GIM was successfully instantiated in TRANSFoRm as the clinical data integration model, and necessary mappings were created to support effective information retrieval for software tools in the project.

Conclusions: We present a novel, unifying approach to address interoperability challenges in heterogeneous data sources, by representing structural and semantic models in one framework. Systems using this architecture can rely solely on the GIM that abstracts over both the structure and coding. Information models, terminologies and mappings are all stored in LexEVS and can be accessed in a uniform manner (implementing the HL7 CTS2 service functional model). The system is flexible and should reduce the effort needed from data sources personnel for implementing and managing the integration.

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Related in: MedlinePlus

Examples of query resolution as applied to TRANSFoRm using clinical data integration model (figure 4), its mappings to the data sources models (figure 5) and terminologies. Highlighted segments represent each level-specific addition based on information from the models served by LexEVS.
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AMIAJNL2012001312F6: Examples of query resolution as applied to TRANSFoRm using clinical data integration model (figure 4), its mappings to the data sources models (figure 5) and terminologies. Highlighted segments represent each level-specific addition based on information from the models served by LexEVS.

Mentions: CDIM was mapped with 44 elements in NPCD and 47 in GPRD. High level classes such as ‘processual entity’ are part of CDIM and are essential to knowledge modeling but are not expected to be used as mapping targets as they are too generic. Twenty-nine mappings (32%) were one-to-one direct relations between CDIM concepts and a data source structural element. The other mappings included concatenation operations and conditional mappings (including related tables). No virtual elements were necessary for the current data source mappings. Figure 5 illustrates an example of a conditional mapping. Precise and comprehensive knowledge of each data source and its real-life usage was essential to achieve satisfactory mappings and query results. Not all fields of the data sources are targets for mappings, nor are all concepts in CDIM mapped to each data source; their coverage typically differs from CDIM. Nevertheless, all the relevant entities for the use cases were successfully mapped. Figure 5 presents those mappings necessary to illustrate the examples in figure 6.


A unified structural/terminological interoperability framework based on LexEVS: application to TRANSFoRm.

Ethier JF, Dameron O, Curcin V, McGilchrist MM, Verheij RA, Arvanitis TN, Taweel A, Delaney BC, Burgun A - J Am Med Inform Assoc (2013)

Examples of query resolution as applied to TRANSFoRm using clinical data integration model (figure 4), its mappings to the data sources models (figure 5) and terminologies. Highlighted segments represent each level-specific addition based on information from the models served by LexEVS.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3756256&req=5

AMIAJNL2012001312F6: Examples of query resolution as applied to TRANSFoRm using clinical data integration model (figure 4), its mappings to the data sources models (figure 5) and terminologies. Highlighted segments represent each level-specific addition based on information from the models served by LexEVS.
Mentions: CDIM was mapped with 44 elements in NPCD and 47 in GPRD. High level classes such as ‘processual entity’ are part of CDIM and are essential to knowledge modeling but are not expected to be used as mapping targets as they are too generic. Twenty-nine mappings (32%) were one-to-one direct relations between CDIM concepts and a data source structural element. The other mappings included concatenation operations and conditional mappings (including related tables). No virtual elements were necessary for the current data source mappings. Figure 5 illustrates an example of a conditional mapping. Precise and comprehensive knowledge of each data source and its real-life usage was essential to achieve satisfactory mappings and query results. Not all fields of the data sources are targets for mappings, nor are all concepts in CDIM mapped to each data source; their coverage typically differs from CDIM. Nevertheless, all the relevant entities for the use cases were successfully mapped. Figure 5 presents those mappings necessary to illustrate the examples in figure 6.

Bottom Line: The GIM was successfully instantiated in TRANSFoRm as the clinical data integration model, and necessary mappings were created to support effective information retrieval for software tools in the project.Information models, terminologies and mappings are all stored in LexEVS and can be accessed in a uniform manner (implementing the HL7 CTS2 service functional model).The system is flexible and should reduce the effort needed from data sources personnel for implementing and managing the integration.

View Article: PubMed Central - PubMed

Affiliation: INSERM UMR936, Université de Rennes 1, Rennes, France.

ABSTRACT

Objective: Biomedical research increasingly relies on the integration of information from multiple heterogeneous data sources. Despite the fact that structural and terminological aspects of interoperability are interdependent and rely on a common set of requirements, current efforts typically address them in isolation. We propose a unified ontology-based knowledge framework to facilitate interoperability between heterogeneous sources, and investigate if using the LexEVS terminology server is a viable implementation method.

Materials and methods: We developed a framework based on an ontology, the general information model (GIM), to unify structural models and terminologies, together with relevant mapping sets. This allowed a uniform access to these resources within LexEVS to facilitate interoperability by various components and data sources from implementing architectures.

Results: Our unified framework has been tested in the context of the EU Framework Program 7 TRANSFoRm project, where it was used to achieve data integration in a retrospective diabetes cohort study. The GIM was successfully instantiated in TRANSFoRm as the clinical data integration model, and necessary mappings were created to support effective information retrieval for software tools in the project.

Conclusions: We present a novel, unifying approach to address interoperability challenges in heterogeneous data sources, by representing structural and semantic models in one framework. Systems using this architecture can rely solely on the GIM that abstracts over both the structure and coding. Information models, terminologies and mappings are all stored in LexEVS and can be accessed in a uniform manner (implementing the HL7 CTS2 service functional model). The system is flexible and should reduce the effort needed from data sources personnel for implementing and managing the integration.

Show MeSH
Related in: MedlinePlus