Building Bridges for Federated Learning in Healthcare: Review on Approaches for Common Data Model Development
: von Gerich, Hanna; Chomutare, Taridzo; Peltonen, Laura-Maria
: Gillian Strudwick, Nicholas R. Hardiker, Glynda Rees, Robyn Cook, Young Ji Lee
: International Congress on Nursing Informatics
Publisher: IOS Press
: 2024
Studies in Health Technology and Informatics
: Innovation in Applied Nursing Informatics
: 315
: 711
: 712
: 978-1-64368-527-4
: 0926-9630
: 1879-8365
DOI: https://doi.org/10.3233/SHTI240292
: https://doi.org/10.3233/shti240292
: https://research.utu.fi/converis/portal/detail/Publication/515530794
Common data models provide a standardized way to represent data used in federated learning tasks. The aim of this review was to explore the development and use of common data models to harmonize electronic health record data in health research. The data search yielded 724 records, of which 19 were included for this study. None of the research focused on nursing specific topics. All studies either utilized the Observational Medical Outcomes Partnership (OMOP) common data model, or developed a model partly based on the OMOP. A roadmap to guide research for the development of common data models for federated learning are warranted.
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This work is supported by Nordic Innovation.