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  4. Common data model for COVID-19 datasets
 
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2022
Journal Article
Title

Common data model for COVID-19 datasets

Abstract
MOTIVATION: A global medical crisis like the coronavirus disease 2019 (COVID-19) pandemic requires interdisciplinary and highly collaborative research from all over the world. One of the key challenges for collaborative research is a lack of interoperability among various heterogeneous data sources. Interoperability, standardization and mapping of datasets are necessary for data analysis and applications in advanced algorithms such as developing personalized risk prediction modeling.
RESULTS: To ensure the interoperability and compatibility among COVID-19 datasets, we present here a common data model (CDM) which has been built from 11 different COVID-19 datasets from various geographical locations. The current version of the CDM holds 4639 data variables related to COVID-19 such as basic patient information (age, biological sex and diagnosis) as well as disease-specific data variables, for example, Anosmia and Dyspnea. Each of the data variables in the data model is associated with specific data types, variable mappings, value ranges, data units and data encodings that could be used for standardizing any dataset. Moreover, the compatibility with established data standards like OMOP and FHIR makes the CDM a well-designed CDM for COVID-19 data interoperability.
AVAILABILITY AND IMPLEMENTATION: The CDM is available in a public repo here: https://github.com/Fraunhofer-SCAI-Applied-Semantics/COVID-19-Global-Model.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Author(s)
Wegner, Philipp
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Jose, Geena Mariya
Lage-Rupprecht, Vanessa
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Golriz Khatami, Sepehr  
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Zhang, Bide
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Springstubbe, Stephan  
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Jacobs, Marc  
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Linden, Thomas  
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Ku, Cindy
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Schultz, Bruce  
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Hofmann-Apitius, Martin  
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Tom Kodamullil, Alpha
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Journal
Bioinformatics  
Open Access
DOI
10.1093/bioinformatics/btac651
Language
English
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
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