TY  - JOUR
SN  - 2045-2322
UR  - https://doi.org/10.1038/s41598-024-72894-y
PB  - Springer Science and Business Media LLC
ID  - discovery10199444
N2  - 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 in place of real data for a wide range of use cases. However, the role that synthetic data might play in all aspects of clinical model development remains unknown. In this work, we used state-of-the-art generators explicitly designed for privacy preservation to create a synthetic version of ever-smokers in the UK Biobank before building prognostic models for lung cancer under several data release assumptions. We demonstrate that synthetic data can be effectively used throughout the medical prognostic modeling pipeline even without eventual access to the real data. Furthermore, we show the implications of different data release approaches on how synthetic biobank data could be deployed within the healthcare system.
KW  - Synthetic data
KW  -  Machine learning
KW  -  Risk-prediction
KW  -  Outcomes research
KW  -  Translational research
A1  - Qian, Zhaozhi
A1  - Callender, Thomas
A1  - Cebere, Bogdan
A1  - Janes, Sam M
A1  - Navani, Neal
A1  - van der Schaar, Mihaela
JF  - Scientific Reports
VL  - 14
AV  - public
Y1  - 2024/10/27/
TI  - Synthetic data for privacy-preserving clinical risk prediction
N1  - © The Author(s), 2024. 
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ER  -