Synthetic data for privacy-preserving clinical risk prediction

Synthetic data for privacy-preserving clinical risk prediction

Abstract Synthetic data promise privacy-preserving data sharing for healthcare research and development. Compared with other privacy-enhancing approaches—such as federated learning—analyses performed on synthetic data can be applied downstream without modification, such that synthetic data can act i...

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Bibliographic Details
Main Authors: Zhaozhi Qian, Thomas Callender, Bogdan Cebere, Sam M. Janes, Neal Navani, Mihaela van der Schaar
Format: Article
Language:English
Published: Nature Portfolio 2024-10-01
Series:Scientific Reports
Subjects:
Synthetic data
Machine learning
Risk-prediction
Online Access:https://doi.org/10.1038/s41598-024-72894-y
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