"Our COMPOSER model uses real-time data in order to predict sepsis before obvious clinical manifestations," said study co-author Gabriel Wardi from the University of California (UC) San Diego School of Medicine, US. "It works silently and safely behind the scenes, continuously surveilling every patient for signs of possible sepsis," Wardi said.
"These advanced AI algorithms can detect patterns that are not initially obvious to the human eye," said study co-author Shamim Nemati, an associate professor at UC San Diego School of Medicine. "The system can look at these risk factors and come up with a highly accurate prediction of sepsis. Conversely, if the risk patterns can be explained by other conditions with higher confidence, then no alerts will be sent," Nemati said.
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