Data files are the building materials we work with every day, all day. Working with them should be effortless.
Have you ever tried to reproduce someone else’s analysis and got different results?
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.
Helping reduce idle time in the USZ Radiology department
Comparing neural-networks versus logistic regression for predicting readmission.
Assessing the quality of online health information with AI.
Novel computational method for drug-drug interaction predictions which are an important consideration for patient treatment.
Using deep learning for automatically generated medical reports describing radiological images.
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.
We are currently looking for Master’s students in the field of bioinformatics for the following topics
Data integration in Cancer Genomics The project’s aim is to characterize the effects of transcriptional and epigenetic processes on mutational patterns in cancer. The student should have basic experience with Unix systems and some experience with at least one scripting language (e.g. Python or R). Prior experience with genomics software and data formats is an advantage.