The study, published in the journal Computers, Materials & Continua, also revealed the best indicators of future severity, and found that they were not as expected.
“While work remains to further validate our model, it holds promise as another tool to predict the patients most vulnerable to the virus, but only in support of physicians’ hard-won clinical experience in treating viral infections,” said Megan Coffee, a clinical assistant professor at New York University (NYU) in the US.
“Our goal was to design and deploy a decision-support tool using AI capabilities — mostly predictive analytics — to flag future clinical coronavirus severity,” said Anasse Bari, a clinical assistant professor at New York University.
“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 said.
For the study, demographic, laboratory, and radiological findings were collected from 53 patients as each tested positive in January for the SARS-CoV2 virus at the two Chinese hospitals.
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.
The goal of the study was to determine whether AI techniques could help to accurately predict which patients with the virus would go on to develop Acute Respiratory Distress Syndrome (ARDS), the fluid build-up in the lungs that can be fatal in the elderly.
The researchers designed computer models that make decisions based on the data fed into them, with programmes getting “smarter” the more data they consider.
They tracked series of decisions between options, and that model the potential consequences of choices at each step in a pathway.
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.
The new AI tool found that changes in three features — levels of the liver enzyme alanine aminotransferase (ALT), reported myalgia, and hemoglobin levels — were most accurately predictive of subsequent, severe disease.
The team reported being able to predict risk of ARDS with up to 80 percent accuracy.
ALT levels — which rise dramatically as diseases like hepatitis damage the liver — were only a bit higher in patients with COVID-19, researchers said, but still featured prominently in prediction of severity.
Deep muscle aches (myalgia) were also more commonplace, and have been linked by past research to higher general inflammation in the body, they said.
Higher levels of hemoglobin, the iron-containing protein that enables blood cells to carry oxygen to bodily tissues, were also linked to later respiratory distress, according to the researchers.