The science behind identifying and preventing harm to patients
At UMC we work to develop the best possible tools for pharmacovigilance in general and in support of all member countries in the WHO PIDM.
Research at UMC focuses on inventing, implementing, and advancing methods for data-driven discovery in support of signal work and for intelligence augmentation – where computational methods support and enhance human decision-making throughout post-market surveillance.
Data-driven Discovery
By letting data lead the way, we can find new and effective ways to detect rare and unexpected side effects to medicines.
Natural Language Processing
UMC is advancing pharmacovigilance science by combining knowledge of NLP methodologies with clinical expertise.
Patient Records
Pioneering methods to identify and explore possible adverse effects in longitudinal patient records broadens the scope of pharmacovigilance.
