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Sepsis, an extreme immune system response to infection in the body, is the cause of death for 1 in 3 people who die in a hospital. If sepsis is diagnosed in the emergency room, probably the best-case scenario is to pray because the survival rate is extremely low," says Sergey Aitan, teaching professor in Northeastern University's Multidisciplinary Engineering Graduate Programs on the Oakland campus and a lead investigator in the project. Researchers used patient data like severe fever, chills, breathing difficulty, skin discoloration, fatigue and confusion captured when a patient is at home, on the way to the hospital and in the emergency room to train a machine learning model to predict sepsis. In practice, Aitan says, emergency room physicians would type or speak into their phones to enter patient information in any language and the system predicts the likelihood that the patient will develop sepsis."