Emory researchers use AI to predict sepsis onset in ICUs

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A team of researchers from Atlanta-based Emory University developed an artificial intelligence algorithm to predict a patient's likelihood of developing sepsis in real-time, according to a study published in the journal Critical Care Medicine.

To create the algorithm, the researchers considered vital sign and EMR data from 27,000 intensive care unit admissions at two Emory University hospitals. The algorithm analyzes 65 key variables to predict a patient's likelihood of developing sepsis after 12, eight, six and four hours.

The researchers validated the algorithm with data from 42,000 patients held in the publicly available Medical Information Mart for Intensive Care-III ICU database.

Although the study authors acknowledged a prospective study is needed to determine the clinical utility of the prediction model, they concluded, "Using data available in the ICU in real-time, [the algorithm] can accurately predict the onset of sepsis in an ICU patient four to 12 hours prior to clinical recognition."

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