A reinforcement learning approach to synthetic data generation.

Natalia Espinosa-Dice¹, Nicholas J Jackson², Chao Yan², Aaron Lee³, Bradley A Malin⁴

¹Department of Computer Science, Princeton University, Princeton, NJ, USA. Electronic address: ne3496@princeton.edu.

²Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.

³John F. Hardesty Department of Ophthalmology and Visual Sciences, Washington University, St. Louis, MO, USA.

⁴Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Computer Science, Vanderbilt University, Nashville, TN, USA; Department of Electrical &amp, Computer Engineering, Vanderbilt University, Nashville, TN, USA.

PubMed DOI

Abstract

OBJECTIVE: Synthetic data generation (SDG) is a promising approach for enabling data sharing in biomedical studies while preserving patient privacy. Yet, state-of-the-art generative models often require large datasets and complex training procedures, limiting their applicability in small-sample settings common in biomedical research. This study aims to develop a more principled and efficient appro…

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