CLEO closed loop framework for synthesizing medical privacy preserving tabular data.

Abstract

The sharing of patient-level structured data is strictly constrained by privacy regulations and governance, creating "data silos" that hinder multi-center research. We propose the CLEO (Clean-Learn-Evaluate-Optimize), a closed-loop framework that integrates a Gaussian Mixture Model generator with Q-learning-based optimization to formalize data synthesis as a Markov Decision Process. Experimental results on a multicenter intracranial aneurysm dataset show that CLEO achieved a combined score of 0.9232 ± 0.0124, outperforming the evaluated representative comparison methods, including TVAE, Gaussian Copula, and CTGAN. In downstream TSTR evaluation, models trained on CLEO-generated data achieved an average AUC of 0.7376, indicating that the synthetic data retained useful clinical decision signals. However, Macro-F1 results also suggest that minority-class prediction remains affected by the imbalanced class distribution. Empirical privacy auditing showed a Nearest Neighbor Adversarial Accuracy score of 0.4940, suggesting low observed re-identification risk under the adopted nearest-neighbor audit setting. These findings indicate that CLEO provides a controllable and empirically auditable framework for supporting cross-institutional research when real-world medical data cannot be directly aggregated.