Dealing with Privacy, Bias and Drift in Synthetic Primary Care Data

Explore the challenges and solutions in utilizing primary healthcare data for disease modeling while addressing privacy concerns in this 40-minute virtual talk by Allan Tucker from the Alan Turing Institute. Delve into the potential of synthetic data generation as a means to mimic real data structures while preserving individual privacy. Learn about the collaborative efforts between Brunel University London and the UK's Medicine and Healthcare products Regulatory Agency in developing a high-fidelity synthetic data service. Examine key issues such as bias in comprehensive national data and concept drift in evolving healthcare models. Gain insights into strategies for better representing the UK population and handling data evolution over time. Aimed at clinicians, policymakers, and health data science researchers, this talk offers valuable knowledge on defining multiple long-term conditions and applying innovative approaches to healthcare data analysis.

AIM RSF - How to Deal with Privacy, Bias & Drift in Synthetic Primary Care Data