Our team uses statistical and deep learning approaches to combine disease trajectories and genetic information with the goal to improve public health interventions.
Our group is interested in finding new ways to early identify common preventable diseases. To do that we develop statistical and deep learning approaches and apply them to millions of health information from electronic health record/national health registries. We then integrate registry-based information with genetic information from large biobank-based studies (e.g. Finngen) to help identify groups of individuals that can most benefit from existing pharmacological interventions. Finally, we aim to implement these approaches in the clinic and evaluate their cost-effectiveness. We are also interested in using trans-national Scandinavian registries to ask basic questions about human nature/nurture and evolution. For example, we are interested in understanding which disease are currently under strongest selection and if we can see the impact of selection within large-scale genetic data.