Computational Medicine is a new field of medicine at the interface between data generating (mainly diagnostic) medical disciplines (e.g. "-omics") and data science (informatics, statistics). It provides information on patients, their health and underlying processes and pathways by exhaustive computational and statistical methodology, that exceed the capacities of conventional approaches.
Computational medicine tries to generate and assess models from existing data to predict/classify/diagnose new patients. It uses large-scale standard modeling approaches (GLMs, GEEs &c.), but also "deep" machine learning and digitally resembles complex decision-finding processes that today depend on physician's experience, knowledge, and intuition.
What we do
- Predictive modeling of clinically releant outcomes from laboratory data
- Deep evaluation of "-omics" data, especially Metabolomics
- Develop and apply tools for laboratory medicine
- Computational/statistical support for clinical studies involving "big data"
Our group is funded by the Swiss Personal Health Network as well as the Swiss National Science Foundation and the Bern Center of Precision Medicine