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Using an Ontology to Derive a Sharable and Interoperable Relational Data Model for Heterogeneous Healthcare Data and Various Applications

Background  A large volume of heavily fragmented data is generated daily in different healthcare contexts and is stored using various structures with different semantics. This fragmentation and heterogeneity make secondary use of data a challenge. Data integration approaches that derive a common dat...

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Autores principales: Khnaisser, Christina, Lavoie, Luc, Fraikin, Benoit, Barton, Adrien, Dussault, Samuel, Burgun, Anita, Ethier, Jean-François
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Georg Thieme Verlag KG 2022
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9788910/
https://www.ncbi.nlm.nih.gov/pubmed/35709746
http://dx.doi.org/10.1055/a-1877-9498
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author Khnaisser, Christina
Lavoie, Luc
Fraikin, Benoit
Barton, Adrien
Dussault, Samuel
Burgun, Anita
Ethier, Jean-François
author_facet Khnaisser, Christina
Lavoie, Luc
Fraikin, Benoit
Barton, Adrien
Dussault, Samuel
Burgun, Anita
Ethier, Jean-François
author_sort Khnaisser, Christina
collection PubMed
description Background  A large volume of heavily fragmented data is generated daily in different healthcare contexts and is stored using various structures with different semantics. This fragmentation and heterogeneity make secondary use of data a challenge. Data integration approaches that derive a common data model from sources or requirements have some advantages. However, these approaches are often built for a specific application where the research questions are known. Thus, the semantic and structural reconciliation is often not reusable nor reproducible. A recent integration approach using knowledge models has been developed with ontologies that provide a strong semantic foundation. Nonetheless, deriving a data model that captures the richness of the ontology to store data with their full semantic remains a challenging task. Objectives  This article addresses the following question: How to design a sharable and interoperable data model for storing heterogeneous healthcare data and their semantic to support various applications? Method  This article describes a method using an ontological knowledge model to automatically generate a data model for a domain of interest. The model can then be implemented in a relational database which efficiently enables the collection, storage, and retrieval of data while keeping semantic ontological annotations so that the same data can be extracted for various applications for further processing. Results  This article (1) presents a comparison of existing methods for generating a relational data model from an ontology using 23 criteria, (2) describes standard conversion rules, and (3) presents O n t o R e l a , a prototype developed to demonstrate the conversion rules. Conclusion  This work is a first step toward automating and refining the generation of sharable and interoperable relational data models using ontologies with a freely available tool. The remaining challenges to cover all the ontology richness in the relational model are pointed out.
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spelling pubmed-97889102022-12-24 Using an Ontology to Derive a Sharable and Interoperable Relational Data Model for Heterogeneous Healthcare Data and Various Applications Khnaisser, Christina Lavoie, Luc Fraikin, Benoit Barton, Adrien Dussault, Samuel Burgun, Anita Ethier, Jean-François Methods Inf Med Background  A large volume of heavily fragmented data is generated daily in different healthcare contexts and is stored using various structures with different semantics. This fragmentation and heterogeneity make secondary use of data a challenge. Data integration approaches that derive a common data model from sources or requirements have some advantages. However, these approaches are often built for a specific application where the research questions are known. Thus, the semantic and structural reconciliation is often not reusable nor reproducible. A recent integration approach using knowledge models has been developed with ontologies that provide a strong semantic foundation. Nonetheless, deriving a data model that captures the richness of the ontology to store data with their full semantic remains a challenging task. Objectives  This article addresses the following question: How to design a sharable and interoperable data model for storing heterogeneous healthcare data and their semantic to support various applications? Method  This article describes a method using an ontological knowledge model to automatically generate a data model for a domain of interest. The model can then be implemented in a relational database which efficiently enables the collection, storage, and retrieval of data while keeping semantic ontological annotations so that the same data can be extracted for various applications for further processing. Results  This article (1) presents a comparison of existing methods for generating a relational data model from an ontology using 23 criteria, (2) describes standard conversion rules, and (3) presents O n t o R e l a , a prototype developed to demonstrate the conversion rules. Conclusion  This work is a first step toward automating and refining the generation of sharable and interoperable relational data models using ontologies with a freely available tool. The remaining challenges to cover all the ontology richness in the relational model are pointed out. Georg Thieme Verlag KG 2022-12-03 /pmc/articles/PMC9788910/ /pubmed/35709746 http://dx.doi.org/10.1055/a-1877-9498 Text en The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. ( https://creativecommons.org/licenses/by/4.0/ ) https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Khnaisser, Christina
Lavoie, Luc
Fraikin, Benoit
Barton, Adrien
Dussault, Samuel
Burgun, Anita
Ethier, Jean-François
Using an Ontology to Derive a Sharable and Interoperable Relational Data Model for Heterogeneous Healthcare Data and Various Applications
title Using an Ontology to Derive a Sharable and Interoperable Relational Data Model for Heterogeneous Healthcare Data and Various Applications
title_full Using an Ontology to Derive a Sharable and Interoperable Relational Data Model for Heterogeneous Healthcare Data and Various Applications
title_fullStr Using an Ontology to Derive a Sharable and Interoperable Relational Data Model for Heterogeneous Healthcare Data and Various Applications
title_full_unstemmed Using an Ontology to Derive a Sharable and Interoperable Relational Data Model for Heterogeneous Healthcare Data and Various Applications
title_short Using an Ontology to Derive a Sharable and Interoperable Relational Data Model for Heterogeneous Healthcare Data and Various Applications
title_sort using an ontology to derive a sharable and interoperable relational data model for heterogeneous healthcare data and various applications
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9788910/
https://www.ncbi.nlm.nih.gov/pubmed/35709746
http://dx.doi.org/10.1055/a-1877-9498
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