AI surveillance tool can successfully predict sepsis risk: Study
The nursing team will then review with the physician and determine appropriate treatment plans, they said.
Representative Image
NEW DELHI: An artificial intelligence (AI) model can quickly identify patients at risk of sepsis infection and potentially help save lives, a study has found.
Sepsis is a serious condition that happens when the body's immune system has an extreme response to an infection.
The study, published in the journal Digital Medicine, found that the AI algorithm, called COMPOSER, resulted in a 17 per cent reduction in mortality.
"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.
Once a patient checks in at the emergency department, the algorithm begins to continuously monitor more than 150 different patient variables that could be linked to sepsis, such as lab results, vital signs, current medications, demographics and medical history.
Should a patient present with multiple variables, resulting in high risk for sepsis infection, the AI algorithm will notify nursing staff via the hospital's electronic health record, the researchers said.
The nursing team will then review with the physician and determine appropriate treatment plans, they 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.
The study examined more than 6,000 patient admissions before and after COMPOSER was deployed in the emergency departments at UC San Diego Medical Center and at Jacobs Medical Center, both in the US.
It is the first study to report improvement in patient outcomes by utilising an AI deep-learning model, which uses artificial neural networks as a check and balance in order to safely, and correctly, identify health concerns in patients.
The model is able to identify complex and multiple risk factors, which are then reviewed by the health care team for confirmation, the researchers said.
"It is because of this AI model that our teams can provide life-saving therapy for patients quicker," Wardi added.