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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...

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Autores principales: Danese, Mark D., Halperin, Marc, Duryea, Jennifer, Duryea, Ryan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
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.
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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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