"The predictive model learns from historical data to help predict who will develop acute respiratory distress syndrome (ARDS), a severe outcome of COVID-19," read a research article by Jiang Xiangao, Megan Coffee and others published in Computers, Materials & Continua Magazine on Tuesday.
Among all clinical symptoms, "a mildly elevated alanine aminotransferase (ALT) (a liver enzyme), the presence of myalgias (body aches), and elevated hemoglobin (red blood cells), in this order, are the clinical features, on presentation, that are the most predictive," read the article.
Meanwhile, key diagnosis characteristics including fever, lymphopenia and chest imaging were not as predictive of severity, it said.
The coronavirus pandemic has spread rapidly across the world in recent days, with a total of 939,436 confirmed infections and 47,287 deaths as of 2 April, per data from Johns Hopkins University. The US topped the list with 216,722 confirmed cases.
Given the rapid spread and increasing caseloads, there is an urgent need to develop clinical skills to rapidly identify which mild cases could progress to critical illnesses, according to the research.
Based on 53 patients from two hospitals in Wenzhou, East China's Zhejiang Province, the research intended to establish an AI framework with predictive analytics (PA) capabilities applied to real patient data, to provide rapid clinical decision-making support.
AI technology has been widely used during the period of virus prevention and treatment in China, including the use of thermo detectors and disinfection robots.
Though the research did not use a large database, the article noted that overall accuracy among the included cases was 70 to 80 percent.
*This article originally appeared in the Global Times.