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The Generalized Data Model for clinical research
BACKGROUND: Most healthcare data sources store information within their own unique schemas, making reliable and reproducible research challenging. Consequently, researchers have adopted various data models to improve the efficiency of research. Transforming and loading data into these models is a la...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6591926/ https://www.ncbi.nlm.nih.gov/pubmed/31234921 http://dx.doi.org/10.1186/s12911-019-0837-5 |
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author | Danese, Mark D. Halperin, Marc Duryea, Jennifer Duryea, Ryan |
author_facet | Danese, Mark D. Halperin, Marc Duryea, Jennifer Duryea, Ryan |
author_sort | Danese, Mark D. |
collection | PubMed |
description | BACKGROUND: Most healthcare data sources store information within their own unique schemas, making reliable and reproducible research challenging. Consequently, researchers have adopted various data models to improve the efficiency of research. Transforming and loading data into these models is a labor-intensive process that can alter the semantics of the original data. Therefore, we created a data model with a hierarchical structure that simplifies the transformation process and minimizes data alteration. METHODS: There were two design goals in constructing the tables and table relationships for the Generalized Data Model (GDM). The first was to focus on clinical codes in their original vocabularies to retain the original semantic representation of the data. The second was to retain hierarchical information present in the original data while retaining provenance. The model was tested by transforming synthetic Medicare data; Surveillance, Epidemiology, and End Results data linked to Medicare claims; and electronic health records from the Clinical Practice Research Datalink. We also tested a subsequent transformation from the GDM into the Sentinel data model. RESULTS: The resulting data model contains 19 tables, with the Clinical Codes, Contexts, and Collections tables serving as the core of the model, and containing most of the clinical, provenance, and hierarchical information. In addition, a Mapping table allows users to apply an arbitrarily complex set of relationships among vocabulary elements to facilitate automated analyses. CONCLUSIONS: The GDM offers researchers a simpler process for transforming data, clear data provenance, and a path for users to transform their data into other data models. The GDM is designed to retain hierarchical relationships among data elements as well as the original semantic representation of the data, ensuring consistency in protocol implementation as part of a complete data pipeline for researchers. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12911-019-0837-5) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6591926 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-65919262019-07-08 The Generalized Data Model for clinical research Danese, Mark D. Halperin, Marc Duryea, Jennifer Duryea, Ryan BMC Med Inform Decis Mak Technical Advance BACKGROUND: Most healthcare data sources store information within their own unique schemas, making reliable and reproducible research challenging. Consequently, researchers have adopted various data models to improve the efficiency of research. Transforming and loading data into these models is a labor-intensive process that can alter the semantics of the original data. Therefore, we created a data model with a hierarchical structure that simplifies the transformation process and minimizes data alteration. METHODS: There were two design goals in constructing the tables and table relationships for the Generalized Data Model (GDM). The first was to focus on clinical codes in their original vocabularies to retain the original semantic representation of the data. The second was to retain hierarchical information present in the original data while retaining provenance. The model was tested by transforming synthetic Medicare data; Surveillance, Epidemiology, and End Results data linked to Medicare claims; and electronic health records from the Clinical Practice Research Datalink. We also tested a subsequent transformation from the GDM into the Sentinel data model. RESULTS: The resulting data model contains 19 tables, with the Clinical Codes, Contexts, and Collections tables serving as the core of the model, and containing most of the clinical, provenance, and hierarchical information. In addition, a Mapping table allows users to apply an arbitrarily complex set of relationships among vocabulary elements to facilitate automated analyses. CONCLUSIONS: The GDM offers researchers a simpler process for transforming data, clear data provenance, and a path for users to transform their data into other data models. The GDM is designed to retain hierarchical relationships among data elements as well as the original semantic representation of the data, ensuring consistency in protocol implementation as part of a complete data pipeline for researchers. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12911-019-0837-5) contains supplementary material, which is available to authorized users. BioMed Central 2019-06-24 /pmc/articles/PMC6591926/ /pubmed/31234921 http://dx.doi.org/10.1186/s12911-019-0837-5 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Technical Advance Danese, Mark D. Halperin, Marc Duryea, Jennifer Duryea, Ryan The Generalized Data Model for clinical research |
title | The Generalized Data Model for clinical research |
title_full | The Generalized Data Model for clinical research |
title_fullStr | The Generalized Data Model for clinical research |
title_full_unstemmed | The Generalized Data Model for clinical research |
title_short | The Generalized Data Model for clinical research |
title_sort | generalized data model for clinical research |
topic | Technical Advance |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6591926/ https://www.ncbi.nlm.nih.gov/pubmed/31234921 http://dx.doi.org/10.1186/s12911-019-0837-5 |
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