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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Georg Thieme Verlag KG
2022
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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. |
format | Online Article Text |
id | pubmed-9788910 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Georg Thieme Verlag KG |
record_format | MEDLINE/PubMed |
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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