University of Zurich & University Hospital Zurich
$ health = f(data) $
Comparing neural-networks versus logistic regression for predicting readmission.
Novel computational method for drug-drug interaction predictions which are an important consideration for patient treatment.
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.
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.
Clinical Data Science, Translational Bioinformatics, Cancer Genetics
Bioinformatics, Cancer Genomics, Long-read sequencing, cfDNA sequencing
Machine Learning, NLP, Image Processing
Bioinformatics, Computational Pharmacology, Deep Learning
Artificial Intelligence, Causal Inference
Sleeping, Hunting birds and mice, Scratching trees
Jumping all over the place 🦘, Knocking over Chess pieces ♟️♕, remote control 📺 and everything in front of him, Playing soccer ⚽ and chasing butterflies 🦋, Sleeping 😴 in front of laptops 💻, Participating in origami 📄🏮 and crafting activities 🧶🎨
Kinematics of toy mice, Hiding in cardboard boxes
Fluid dynamics, Burrowing under blankets