arXiv Machine Learning
Oct 22

Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests

The study evaluates whether synthetic data can replicate real epidemiological findings using a new method called adversarial random forests (ARF). Six epidemiological analyses—covering topics such as blood pressure, myocardial infarction, and diabetes—were replicated on ARF-generated synthetic data from three large cohorts, and the results consistently matched the original studies. The authors also show that ARF outperforms other tabular synthesizers in terms of utility, privacy, generalization, and computational efficiency, especially when dimensionality and variable complexity are reduced.

By Jan Kapar, Kathrin G\"unther, Lori Ann Vallis, Klaus Berger, Nadine Binder, Hermann Brenner, Stefanie Castell, Beate Fischer, Volker Harth, Bernd Holleczek, Timm Intemann, Till Ittermann, Andr\'e Karch, Thomas Keil, Lilian Krist, Berit Lange, Michael F. Leitzmann, Katharina Nimptsch, Nadia Obi, Iris Pigeot, Tobias Pischon, Tamara Schikowski, B\"orge Schmidt, Carsten Oliver Schmidt, Anja M. Sedlmair, Justine Tanoey, Harm Wienbergen, Andreas Wienke, Claudia Wigmann, Marvin N. Wright
arXiv Machine Learning
Jun 9

Synthetic but Not Realistic: The Evaluation Challenge in Generative Modelling for Structured Electronic Medical Records

arXiv:2606. 08903v1 Announce Type: new Abstract: Synthetic healthcare data are widely proposed as privacy-preserving substitutes for real patient data, yet their evaluation remains dominated by statistical similarity and predictive performance that do not reflect clinical validity.

By Nicholas I-Hsien Kuo, Blanca Gallego, Louisa Jorm