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 …

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

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

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 …

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 …

Background Synthetic data may provide a solution to researchers who wish to generate and
share data in support of precision healthcare. Recent advances in data synthesis enable the …

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 …

Data is the fuel of machine learning algorithms, therefore data generation in machine
learning is becoming an important topic. The problem is that finding enough data for …

Building and testing machine learning models requires access to large and diverse data. But
where can you find usable datasets without running into privacy issues? This practical book …