SYNTHIA

As the name suggests, synthetic data is data that has been artificially generated to mimic real patient data. It is used to train artificial intelligence (AI) algorithms and offers a potential solution to several issues in health research, including a lack of real, high-quality datasets that can be used in research and concerns about patient privacy. However, questions remain regarding the quality of synthetic datasets, and about the best synthetic data generation methods to use in different situations.

The aim of SYNTHIA is to deliver validated, reliable tools and methods for synthetic data generation (SDG). The tools will cover multiple data types including lab results, clinical notes, genomics, imaging and m-health data. SYNTHIA also hopes to make possible the generation of longitudinal data.

To focus its efforts, and to showcase the utility of synthetic data in different areas, the project will address six diseases: two solid tumours (lung cancer and breast cancer), two blood cancers (multiple myeloma and diffuse large B-cell lymphoma), one neurodegenerative disease (Alzheimer’s disease) and one metabolic disease (type 2 diabetes).

The project outputs will be made available to the research community through a dedicated online platform. In addition to synthetic data generation workflows that can be used in different situations, the platform will include assessment frameworks to help users evaluate the synthetic data generated for privacy (risk of re-identification), quality (representativeness and similarity to real data), and applicability (i.e. its suitability for its intended use). Finally, the platform will boast a repository of high-quality synthetic data sets, each of which will be labelled with its suitability for specific applications.

Ultimately, the platform will help to build trust among stakeholders regarding the usefulness of synthetic data, and facilitate the responsible use of synthetic data by the health research community.