Recent progress in artificial intelligence and machine learning has led to the growth of
research in every aspect of life including the health care domain. However, privacy risks and …

Background Machine learning (ML) has made a significant impact in medicine and cancer
research; however, its impact in these areas has been undeniably slower and more limited …

Résumé Les réseaux antagonistes génératifs (RAG) sont des modèles de réseaux
neuronaux de pointe utilisés pour synthétiser des images et d'autres données. Les RAG ont …

Synthetic electronic health records (EHRs) that are both realistic and privacy-preserving
offer alternatives to real EHRs for machine learning (ML) and statistical analysis. However …

Synthetic health data have the potential to mitigate privacy concerns in supporting
biomedical research and healthcare applications. Modern approaches for data generation …

Background: Privacy restrictions limit access to protected patient-derived health information
for research purposes. Consequently, data anonymization is required to allow researchers …

Background There has been growing interest in data synthesis for enabling the sharing of
data for secondary analysis; however, there is a need for a comprehensive privacy risk …

Synthetic data generation has emerged as a promising method to protect patient privacy
while sharing individual-level health data. Intuitively, sharing synthetic data should reduce …

Background A regular task by developers and users of synthetic data generation (SDG)
methods is to evaluate and compare the utility of these methods. Multiple utility metrics have …

Objective Recent studies on electronic health records (EHRs) started to learn deep
generative models and synthesize a huge amount of realistic records, in order to address …