Clinical Data Science

Multilingual de-identification techniques for medical records.

ML models rapidly stratify patient cardiac risk in emergency settings

ML models for predicting patient-related nursing workload.

Deep learning models quantify lung disease progression

Generative DL models patient trajectories

AI-assisted diagnosis in rheumatology

Today, in every aspect of our lives, everything we do leaves a digital footprint. Health-care institutions are also increasingly collecting more data on their patients, thanks to improved IT infrastructure and new sensors that allow to record vital signs at high frequencies. Most hospitals still store and manage this information locally, under strict data privacy and protection regulations. Although data privacy and protection are of the utmost importance, they become an obstacle for the large-scale analysis of such data and the extraction of new insights that could help doctors in the diagnosis and therapeutic process.

Since the early days of computing, healthcare professionals have dreamt of using the vast storage and processing powers of computers to sift through vast medical archives and automatically discover new facts and medical knowledge locked inside electronic health records (EHRs) and similar digital patient data archives. This is not unlike the activity of physicians that use their clinical experience to identify patterns (such as symptoms) that are common among a group of patients, leading to new disease classifications and eventually treatment strategies.

Helping reduce idle time in the USZ Radiology department

Comparing neural-networks versus logistic regression for predicting readmission.