Publication
The RE-SAMPLE Ecosystem for Privacy-Preserving Personalized Healthcare for COPD and Comorbidities: Open Federated Learning Platform, Data, and Models
Jakob Fabian Lehmann; Gesa Wimberg; Serge Autexier; Alberto Acebes; Christos Kalloniatis; Costas Lambrinoudakis; Thrasyvoulos Giannakopoulos; Andreas Menegatos; Agni Delvinioti; Giulio Pagliari; Nicoletta di Giorgi; Karno Raid; Danae Lekka; Aristodemos Pnevmatikakis; Sofoklis Kyriazakos; Konstantina Kostopoulou; Monique Tabak; Roswita Vaseur
In: TBA (Hrsg.). 18th International Joint Conference, BIOSTEC 2025, Revised Selected Papers. Pages 1-22, Communications in Biomedical Engineering Systems and Technologies, Springer Nature, Heidelberg, Germany, 12/2025.
Abstract
Federated learning is gaining increasing traction, including in healthcare applications. The platform presented in this paper, developed by a multidisciplinary consortium, enables privacy-preserving training of machine learning models to generate predictions for patients with chronic obstructive pulmonary disease and comorbidities. In addition, data synchronization and monitoring are facilitated via the HL7 FHIR standard. The platform includes two front ends: a patient-facing smartphone app and a dashboard designed for healthcare professionals, currently in use at three hospitals in Italy, Estonia, and the Netherlands. Source code, synthetic datasets and fitted ML models will be released and indexed on https://zenodo.org. Initial ML results obtained from models trained with the platform are discussed. The overall architecture and its implementation in European hospitals is shown in this paper.
