Researchers from the University of Copenhagen have developed a piece of artificial intelligence-enhanced software they believe can almost certainly predict the chances of a person dying from the new coronavirus after being infected.
The scientists, whose research appeared in Scientific Reports, a peer-reviewed journal published by Nature, say their program, which calculates the chances of a fatal Covid-19 outcome based on a range of patient data, has a 90 percent accuracy rate.
The program’s calculations are based on factors including body mass index, gender and whether or not the patient suffers from high blood pressure, with those three elements weighted most heavily. Other factors, including a person’s age, whether or not they suffer from asthma, chronic obstructive pulmonary disease, a neurological disease, or heart disease, are also taken into account.
Researchers used data from Denmark’s Capital Region, which includes Copenhagen, as well as Region Zealand, situated to its south, collecting information from 3,944 patients with a confirmed Covid-19 diagnosis. The program was then trained to find patterns between patients’ prior maladies and their reaction to the coronavirus infection. Scientists processed the data using the Computerome, the University of Copenhagen supercomputer for life sciences.
Along with the 90 percent accuracy in determining potentially fatal outcomes, the program is said to be 80 percent accurate in finding out whether a patient will need access to a respirator.
“Our results demonstrate, unsurprisingly, that age and BMI are the most decisive parameters for how severely a person will be affected by COVID-19. But the likelihood of dying or ending up on a respirator is also heightened if you are male, have high blood pressure or a neurological disease,” Dr. Mads Nielsen, study coauthor and professor at the University of Copenhagen’s department of computer science, says.
Neilson stresses that the research can literally have life-saving implications, as it could identify individuals who require a ventilator, or determine who should be given priority to receive a vaccine. It can also assist overburdened hospital staff in figuring out who should receive urgent priority for care.
