What we do
The primary research interest of our group lies at the intersection of machine learning and medicine. We work on advancing and developing novel machine learning techniques for precision medicine, the life sciences and clinical data analysis. The field of action comprises many areas such as prediction of response to treatment in personalized medicine, (sparse) biomarker detection, tumor classification or the understanding of interactions between genes or groups of genes. We approach these challenges data and problem driven. In close collaboration with physicians we identify gaps where current technologies fail and develop tailored solutions.
Find out about the Group’s activities
Main publications 2020
- Sutter T M et al.
Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence
NeurIPS, preprints - Gotta V et al.
Identifying key predictors of mortality in young patients on chronic haemodialysis—a machine learning approach
Nephrology Dialysis Transplantation, 10.1093/ndt/gfaa128 - Sutter T M et al.
A comparison of general and disease-specific machine learning models for the prediction of unplanned hospital readmissions
Journal of the American Medical Informatics Association, 10.1093/jamia/ocaa299
