Washington – Researchers in the U.S. and China reported Monday they have developed an artificial intelligence tool that is able to accurately predict which newly infected patients with the novel coronavirus go on to develop severe lung disease. Once deployed, the algorithm could assist doctors in making choices about where to prioritize care in resource-stretched health care systems, said Megan Coffee, a physician and professor at New York University's Grossman School of Medicine who co-authored a paper on the finding in the journal Computers, Materials & Continua. The tool discovered several surprising indicators that were most strongly predictive of who went on to develop so-called acute respiratory disease syndrome (ARDS), a severe complication of the COVID-19 illness that fills the lungs with fluid and kills around 50 percent of coronavirus patients who get it. The team applied a machine learning algorithm to data from 53 coronavirus patients across two hospitals in Wenzhou, China, finding that changes in three features – levels of the liver enzyme alanine aminotransferase (ALT), reported body aches, and hemoglobin levels – were most accurately predictive of subsequent, severe disease. Using this information along with other factors, the tool was able to predict risk of ARDS with up to 80 percent accuracy.