Symptoms were typically mild to begin with, including cough, fever and stomach upset.
In a minority of patients, however, severe symptoms developed with a week, including pneumonia.
For the new study, the researchers designed computer models that make decisions based on the data fed into them, with programmes getting “smarter” the more data they consider.
The researchers were surprised to find that characteristics considered to be hallmarks of COVID-19, like certain patterns seen in lung images, fever, and strong immune responses, were not useful in predicting which of the many patients with initial, mild symptoms would go to develop severe lung disease.
Neither were age and gender helpful in predicting serious disease, although past studies had found men over 60 to be at higher risk.
Instead, the new AI tool found that changes in three features - levels of the liver enzyme alanine aminotransferase (ALT), reported myalgia, and haemoglobin levels - were most accurately predictive of subsequent, severe disease.
Together with other factors, the team reported being able to predict risk of Acute Respiratory Distress Syndrome or ARDS with up to 80 per cent accuracy.
“We hope that the tool, when fully developed, will be useful to physicians as they assess which moderately ill patients really need beds, and who can safely go home, with hospital resources stretched thin,” Bari added.