Published on in Vol 3 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/47652, first published .
Privacy-Preserving Federated Survival Support Vector Machines for Cross-Institutional Time-To-Event Analysis: Algorithm Development and Validation

Privacy-Preserving Federated Survival Support Vector Machines for Cross-Institutional Time-To-Event Analysis: Algorithm Development and Validation

Privacy-Preserving Federated Survival Support Vector Machines for Cross-Institutional Time-To-Event Analysis: Algorithm Development and Validation

Julian Späth   1 , MSc ;   Zeno Sewald   2 , BSc ;   Niklas Probul   1 , MSc ;   Magali Berland   3 , PhD ;   Mathieu Almeida   3 , PhD ;   Nicolas Pons   3 , PhD ;   Emmanuelle Le Chatelier   3 , PhD ;   Pere Ginès   4, 5, 6, 7 , MD, PhD ;   Cristina Solé   4, 5, 6 , MD ;   Adrià Juanola   4, 5, 6 , MD, PhD ;   Josch Pauling   2 , PhD ;   Jan Baumbach   1 , Prof Dr

1 Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany

2 LipiTUM, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany

3 MetaGenoPolis, INRAE, Université Paris-Saclay, Jouy-en-Josas, France

4 Liver Unit, Hospital Clínic de Barcelona, Barcelona, Spain

5 Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain

6 Centro de Investigacion en Red de Enfermedades hepaticas y Digestivas (CIBEReHD), Madrid, Spain

7 Faculty of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain

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