  The biomedical field is among the sectors most impacted by the increasing
regulation of Artificial Intelligence (AI) and data protection legislation,
given the sensitivity of patient information. However, the rise of synthetic
data generation methods offers a promising opportunity for data-driven
technologies. In this study, we propose a statistical approach for synthetic
data generation applicable in classification problems. We assess the utility
and privacy implications of synthetic data generated by Kernel Density
Estimator and K-Nearest Neighbors sampling (KDE-KNN) within a real-world
context, specifically focusing on its application in sepsis detection. The
detection of sepsis is a critical challenge in clinical practice due to its
rapid progression and potentially life-threatening consequences. Moreover, we
emphasize the benefits of KDE-KNN compared to current synthetic data generation
methodologies. Additionally, our study examines the effects of incorporating
synthetic data into model training procedures. This investigation provides
valuable insights into the effectiveness of synthetic data generation
techniques in mitigating regulatory constraints within the biomedical field.
