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.
We are proud to be part of the UZH University Research Priority Programs (URPP) where we with the Schwank lab investigate the use of AI 🐱💻 and CRISPR-based technologies ✂️ 🧬 for germline editing. Moreover, these efforts aim at providing policy guidance on the societal impact and legal challenges of rapidly changing medical technology on human reproduction. The goals and objectives of the subproject SP4 CRISPR Technology in Human Reproduction we are working on are detailed below (taken from the URPP Human Reproduction Reloaded official website ).
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.
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.