Data Science

AutoDiscern: Rating the Quality of Online Health Information with Hierarchical Encoder Attention-based Neural Networks

To help patients find high quality health information online, we developed a Deep Learning system that evaluates the quality of online health articles. The system implements the DISCERN criteria, which checks for references, balanced writing, and more.

30 days All-Cause Readmission

Comparing neural-networks versus logistic regression for predicting readmission.


Assessing the quality of online health information with AI.

Drug-Drug Interactions

Novel computational method for drug-drug interaction predictions which are an important consideration for patient treatment.

Automated Reports from Xrays

Using deep learning for automatically generated medical reports describing radiological images.

Neural networks versus Logistic regression for 30 days all-cause readmission prediction

We conclude that data from patient timelines improve 30 day readmission prediction, that a logistic regression with LASSO has equal performance to the best neural network model and that the use of administrative data result in competitive performance compared to published approaches based on richer clinical datasets.

Postdoc Opening in Machine Learning in Biomedicine

Summary The University of Zurich together with the University Hospital of Zurich are embarking on a concerted effort to develop informatics programs to advance biomedical research using cutting edge computational approaches. As part of these efforts, the Krauthammer research group investigates topics in clinical data science and translational bioinformatics, such as knowledge discovery from Big Data sources (Electronic Medical Record), development of Natural Language processing, information retrieval and extraction routines, as well as the analysis of human Omics data.

We are looking for Masters Students!

We are currently looking for Master’s students in the field of bioinformatics for the following topics Analyzing the effects of DNA secondary structure on the mutational pattern in cancer. The project’s aim is to uncover the relationship between mutational patterns detected in various cancer types and the locations of non-B-DNA secondary structures in the human genome. The student should have basic experience with Unix systems and some experience with at least one scripting language (e.