Privacy-preserving dataset combination and Lasso regression for healthcare predictions

Privacy-preserving dataset combination and Lasso regression for healthcare predictions

Abstract Background Recent developments in machine learning have shown its potential impact for clinical use such as risk prediction, prognosis, and treatment selection. However, relevant data are often scattered across different stakeholders and their use is regulated, e.g. by GDPR or HIPAA. As a c...

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Bibliographic Details
Main Authors: Marie Beth van Egmond, Gabriele Spini, Onno van der Galien, Arne IJpma, Thijs Veugen, Wessel Kraaij, Alex Sangers, Thomas Rooijakkers, Peter Langenkamp, Bart Kamphorst, Natasja van de L’Isle, Milena Kooij-Janic
Format: Article
Language:English
Published: BMC 2021-09-01
Series:BMC Medical Informatics and Decision Making
Subjects:
Secure multi-party computation
Privacy
Machine learning
Lasso regression
Online Access:https://doi.org/10.1186/s12911-021-01582-y

Internet

https://doi.org/10.1186/s12911-021-01582-y

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