Electronic healthcare record data have been used to study risk factors of disease, treatment
effectiveness and safety, and to inform healthcare service planning. There has been …

There is a growing demand for the uptake of modern artificial intelligence technologies
within healthcare systems. Many of these technologies exploit historical patient health data …

There is increasing interest in the potential of synthetic data to validate and benchmark
machine learning algorithms as well as reveal any biases in real-world data used for …

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 …

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

Synthetic data provides a privacy protecting mechanism for the broad usage and sharing of
healthcare data for secondary purposes. It is considered a safe approach for the sharing of …

Synthetic data is a rapidly evolving field with growing interest from multiple industry
stakeholders and European bodies. In particular, the pharmaceutical industry is starting to …

The increasing prevalence of chronic non-communicable diseases makes it a priority to
develop tools for enhancing their management. On this matter, Artificial Intelligence …

Synthetic data generation is the process of using machine learning methods to train a model
that captures the patterns in a real dataset. Then new or synthetic data can be generated …

Electronic health records (EHR) contain a lot of valuable information about individual
patients and the whole population. Besides structured data, unstructured data in EHRs can …