CPRD has generated high-fidelity synthetic datasets using a synthetic data generation and evaluation framework that was developed under a grant from the Regulators’ Pioneer Fund launched by The Department for Business, Energy and Industrial Strategy (BEIS) and managed by Innovate UK. The synthetic data generation and evaluation framework used to generate this synthetic dataset and the synthetic datasets are owned by the Medicines and Healthcare products Regulatory Agency (MHRA).
These high-fidelity synthetic datasets replicate the complex clinical relationships in real primary care patient data while protecting patient privacy as they are wholly synthetic. They can be used instead of real patient data for complex statistical analyses as well as machine learning and artificial intelligence (AI) research applications.
These synthetic datasets are being made available with a nominal administrative fee and will need a data sharing agreement (DSA) with the applicant’s organisation for access in line with advice received from the Information Commissioner’s Office (ICO) Innovation Hub in response to a formal query by the MHRA.
For access to these datasets please submit an application form to firstname.lastname@example.org including ‘Synthetic data access request’ in the email subject header. Applicants from organisations that are not existing CPRD clients will also need to submit a new client request form.
CPRD cardiovascular disease synthetic dataset
This synthetic dataset is based on anonymised real primary care patient data extracted from the CPRD Aurum database. The dataset focuses on cardiovascular disease risk factors and was a proof-of-concept dataset developed as part of a project funded by the Regulators’ Pioneer Fund launched by The Department for Business, Energy and Industrial Strategy (BEIS) and managed by Innovate UK.
CPRD COVID-19 symptoms and risk factors synthetic dataset
This synthetic dataset is based on anonymised real primary care patient data extracted from the CPRD Aurum database. The dataset focuses on patients presenting to primary care with symptoms indicative of COVID-19 (confirmed/suspected COVID-19) and control patients with negative COVID-19 test results. The dataset includes data on sociodemographic and clinical risk factors.
The development of this dataset was funded by NHSX using the synthetic data generation and evaluation framework developed under a grant from the Regulators’ Pioneer Fund launched by The Department for Business, Energy and Industrial Strategy (BEIS) and managed by Innovate UK.
Further information and publications
Publication: de Benedetti, J., Oues, N., Wang, Z., Myles, P., Tucker, A. (2020). Practical lessons from Generating Synthetic Healthcare Data with Bayesian Networks. In: Koprinska I. et al. (eds) ECML PKDD 2020 Workshops. ECML PKDD 2020. Communications in Computer and Information Science, vol 1323. Springer, Cham. https://doi.org/10.1007/978-3-030-65965-3_3
Publication: Tucker, A., Wang, Z., Rotalinti, Y. et al. Generating high-fidelity synthetic patient data for assessing machine learning healthcare software. npj Digit. Med. 3, 147 (2020). https://doi.org/10.1038/s41746-020-00353-9
Publication: Wang, Z, Myles, P, Tucker, A. Generating and evaluating cross-sectional synthetic electronic healthcare data: Preserving data utility and patient privacy. Computational Intelligence. 2021; 1– 33. https://doi.org/10.1111/coin.12427
Publication: Myles P et al. Synthetic data and the innovation, assessment, and regulation of AI Medical devices. RF Quarterly. 2021; 1(2): 48-53. © 2021. Regulatory Affairs Professional Society.