AI Predicts How Patients with Viral Infections, Including COVID-19, Will Fare

The UCSD School of Medicine campus is shown here on May 1, 2019.
The UCSD School of Medicine campus is shown here on May 1, 2019. (Brandon Quester/inewsource)

UC San Diego School of Medicine researchers used an artificial intelligence algorithm to sift through terabytes of gene expression data — which genes are “on” or “off” during infection — to look for shared patterns in patients with past pandemic viral infections, including SARS, MERS and swine flu, to predict how they fare.

Two telltale signatures emerged from the study, published in Friday’s eBiomedicine.

“These viral pandemic-associated signatures tell us how a person’s immune system responds to a viral infection and how severe it might get, and that gives us a map for this and future pandemics,” said Dr. Pradipta Ghosh, a professor of cellular and molecular medicine at the UCSD School of Medicine and Moores Cancer Center.

Ghosh co-led the study with Debashis Sahoo, assistant professor of pediatrics at UCSD School of Medicine and of computer science and engineering at Jacobs School of Engineering, and Soumita Das, associate professor of pathology at UCSD School of Medicine.

During a viral infection, the immune system releases small proteins called cytokines into the blood. These proteins guide immune cells to the site of infection to help get rid of the infection. Sometimes, though, the body releases too many cytokines, creating a runaway immune system that attacks its own healthy tissue. This mishap, known as a cytokine storm, is believed to be one of the reasons some virally infected patients, including some with the common flu, succumb to the infection while others do not.

Times of San Diego