Harmonization of Electronic Medical Records for Federated Learning: Addressing Challenges in International Healthcare Collaborations
This study highlights key challenges of harmonizing healthcare data and provides an example as a solution. In order to confirm reliability of our dataset for predicting MACE using FL, further testing and validation will be performed.
Authors
- Hilton Vicente César (ORCID: https://orcid.org/0000-0002-2701-8898)
- Stefan Kalabakov (ORCID: https://orcid.org/0009-0002-3124-8845)
- Peter P. Rainer (ORCID: https://orcid.org/0000-0002-2840-6454)
- Stefanie Jauk (ORCID: https://orcid.org/0000-0003-1287-594X)
- Gladys Pierri
- Kátia Mitiko Firmino Suzuki
- Paulo Mazzoncini de Azevedo‐Marques (ORCID: https://orcid.org/0000-0002-7271-2774)
- Diether Kramer
- Michael Schrempf
- Lena Lorenzer
Institutions
- Universidade de São Paulo (BR)
- Medical University of Graz (AT)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- Studies in health technology and informatics
- Published
- 2025-04-24
- DOI
- https://doi.org/10.3233/shti250175
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Georgia Clinical and Translational Science Alliance
- Fundação de Amparo à Pesquisa do Estado de São Paulo
- Bundesministerium für Bildung und Forschung
- Austrian Science Fund
- Vetenskapsrådet
- National Center for Advancing Translational Sciences