A4 Refereed article in a conference publication
Building Bridges for Federated Learning in Healthcare: Review on Approaches for Common Data Model Development
Authors: von Gerich, Hanna; Chomutare, Taridzo; Peltonen, Laura-Maria
Editors: Gillian Strudwick, Nicholas R. Hardiker, Glynda Rees, Robyn Cook, Young Ji Lee
Conference name: International Congress on Nursing Informatics
Publisher: IOS Press
Publication year: 2024
Journal: Studies in Health Technology and Informatics
Book title : Innovation in Applied Nursing Informatics
Volume: 315
First page : 711
Last page: 712
eISBN: 978-1-64368-527-4
ISSN: 0926-9630
eISSN: 1879-8365
DOI: https://doi.org/10.3233/SHTI240292
Publication's open availability at the time of reporting: Open Access
Publication channel's open availability : Partially Open Access publication channel
Web address : https://doi.org/10.3233/shti240292
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/515530794
Self-archived copy's licence: CC BY NC
Self-archived copy's version: Publisher`s PDF
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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Funding information in the publication:
This work is supported by Nordic Innovation.
