Didactic Elective Activity

This course explores the complex landscape of disease progression through time, introducing students to the critical distinction between comorbidity and multimorbidity within the framework of temporal disease trajectories. The course advances through methodologies for descriptive analysis of population-wide life course data, providing insights into disease patterns across large populations and extended timeframes. The course delves into cutting-edge applications of deep machine learning for predicting health outcomes using electronic patient records and for integrating clinical data with multi-omics information.

The final segment addresses the practical challenges of implementing these advanced analytical approaches within existing healthcare infrastructures and clinical trial frameworks, preparing students to bridge the gap between theoretical knowledge and real-world healthcare applications. 

Søren Brunak, Ph.D., is a professor of Disease Systems Biology and Research Director in the Novo Nordisk Foundation Center for Protein Research at University of Copenhagen. His program combines molecular level systems biology data with analysis of healthcare sector phenotypic data (electronic patient records, registry information and biobank questionnaires) to understand multimorbidities and discriminate between treatment related disease correlations. This stratifies patients not only from their genotype, but also based on the clinical descriptions in their medical records and is particularly relevant in the context of the precision medicine agenda. He has been a Member of the Royal Swedish Academy of Sciences since 2016, a Member of the European Molecular Biology Organization since 2009, and a Member of the Royal Danish Academy of Sciences and Letters since 2004.

https://researchprofiles.ku.dk/en/persons/s%C3%B8ren-brunak

The first 50 reservations will be accepted and can be made through the following link: https://forms.gle/yLTpLDTVcQj4jsw17