Nowadays healthcare is increasingly tailored to an individual’s unique genetic profile and care needs. The ambition of personalized healthcare requires and has generated an unprecedented amount of sensitive clinicogenomic data to advance research and health services. A central dilemma for policymakers, industry partners, health providers and researchers alike is how to accelerate these advancements by sharing data while still safeguarding patient’s privacy.
Synthetic data generation, a critical development in data science and artificial intelligence, has the potential to significantly mitigate patient privacy concerns in data sharing while maintaining research value. This modeling technique generates a new dataset with the same statistical properties as the original dataset, but does not compromise the privacy of individuals. Demonstration projects have been undertaken in several industries such as banking and healthcare.
A diversity of audience will find this cross-disciplinary event insightful, no matter whether you are a government official, health administrator, physician, AI enthusiast, biotech veteran, entrepreneur, compliance officer, social scientist or law student. See you there!”