USC researchers have developed a new method to counter emergent mutations of the coronavirus and hasten vaccine development to stop the pathogen responsible for killing thousands of people and ruining the economy.

Using artificial intelligence (AI), the research team at the USC Viterbi School of Engineering developed a method to speed the analysis of vaccines and zero in on the best potential preventive medical therapy.

How much personal information can our phone apps gather through location tracking? To answer this question, two researchers - Mirco Musolesi (University of Bologna, Italy) and Benjamin Baron (University College London, UK) - carried out a field study using an app specifically developed for this research.

When the COVID-19 pandemic struck in early 2020, doctors and researchers rushed to find effective treatments. There was little time to spare. "Making new drugs takes forever," says Caroline Uhler, a computational biologist in MIT's Department of Electrical Engineering and Computer Science and the Institute for Data, Systems and Society, and an associate member of the Broad Institute of MIT and Harvard.

Individual choices in medicine carry a certain amount of uncertainty.

An innovative partnership at The University of Texas at Austin takes aim at medicine down to the individual level by applying state-of-the-art computation to medical care.

Wearable devices can identify COVID-19 cases earlier than traditional diagnostic methods and can help track and improve management of the disease, Mount Sinai researchers report in one of the first studies on the topic. The findings were published in the Journal of Medical Internet Research on January 29.

With the aid of sophisticated machine learning, researchers at UPMC and the University of Pittsburgh School of Medicine demonstrated that a tool they developed can rapidly predict mortality for patients facing transfer between hospitals in order to access higher-acuity care. This research, published today in PLOS One, could help physicians, patients and their families avoid unnecessary hospital transfers and low-value treatments, while better focusing on the goals of care expressed by patients.

A new neural network developed by researchers at the University of Eastern Finland and Kuopio University Hospital enables an easy and accurate assessment of sleep apnoea severity in patients with cerebrovascular disease. The assessment is automated and based on a simple nocturnal pulse oximetry, making it possible to easily screen for sleep apnoea in stroke units.