Background: The exploitation of synthetic data in health care is at an early stage. Synthetic
data could unlock the potential within health care datasets that are too sensitive for release …

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

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 …

Background The use of wearables facilitates data collection at a previously unobtainable
scale, enabling the construction of complex predictive models with the potential to improve …

Background Data science offers an unparalleled opportunity to identify new insights into
many aspects of human life with recent advances in health care. Using data science in …

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 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 …

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 …

Background: Missing data is a challenge for all studies; however, this is especially true for
electronic health record (EHR)-based analyses. Failure to appropriately consider missing …

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 …