Should we synthesize more than we need: impact of synthetic data generation for high-dimensional cross-sectional medical data

Abstract

Abstract Objective In medical research and education, generative artificial intelligence/machine learning (AI/ML) models to synthesize artificial medical data can enable the sharing of high-quality data while preserving the privacy of patients. Given that such data is often high-dimensional, a relevant consideration is whether to synthesize the entire dataset when only a task-relevant subset is needed. This study evaluates how the number of variables in training impacts fidelity, utility, and privacy of the synthetic data (SD). Material and Methods We used 12 cross-sectional medical datasets, defined a downstream task with corresponding core variables, and derived 6354 variants by adding adjunct variables to the core. SD was generated using 7 different generative models and evaluated for fidelity, downstream utility, and privacy. Mixed-effect models were used to assess the effect of adjunct variables on the respective evaluation metric, accounting for the medical dataset as a random component. Results Fidelity was unaffected by the number of adjunct variables in 5/7 SDG models. Similarly, downstream utility remained stable in 6/7 (predictive task) and 5/7 (inferential task) SDG models. Where significant effects were observed, they were minimal, resulting, for example, in a 0.05 decrease in Area under the Receiver Operating Characteristic curve (AUROC) when adding 120 variables. Privacy was not impacted by the number of adjunct variables. Discussion Our findings show that fidelity, utility, and privacy are preserved when generating a more comprehensive medical dataset than the task-relevant subset. Conclusion Our findings support a cost-effective, utility, and privacy-preserving way of implementing SDG into medical research and education.

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