This paper is in the following e-collection/theme issue:

AI Data (13) Artificial Intelligence (1556) Ethical, Legal, and Social Issues in AI (43)

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

Authors of this article:

Julian Späth1 Author Orcid Image ;   Zeno Sewald2 Author Orcid Image ;   Niklas Probul1 Author Orcid Image ;   Magali Berland3 Author Orcid Image ;   Mathieu Almeida3 Author Orcid Image ;   Nicolas Pons3 Author Orcid Image ;   Emmanuelle Le Chatelier3 Author Orcid Image ;   Pere Ginès4, 5, 6, 7 Author Orcid Image ;   Cristina Solé4, 5, 6 Author Orcid Image ;   Adrià Juanola4, 5, 6 Author Orcid Image ;   Josch Pauling2 Author Orcid Image ;   Jan Baumbach1 Author Orcid Image
Article Authors Cited by (1) Tweetations (4) Metrics

Altmetric

Altmetric discovers Social Media mentions. Click the ‘See more details’ link for a full report.

Dimensions

Dimensions discovers Citations. Click the ‘details’ link for a full report.

Metrics Since Publication

Download .csv
Daily | Monthly | Annually