Preventing deadly hospital infections with machine learning

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Nearly 30,000 Americans die each year from an aggressive, gut-infecting bacteria called Clostridium difficile (C. New machine learning models tailored to individual hospitals could give them a much earlier prediction of which patients are most likely to develop C. difficile, potentially helping them stave off infection before it starts. The models are detailed in a paper published today in Infection Control and Hospital Epidemiology. Developed by researchers at the University of Michigan, Massachusetts General Hospital and MIT, the models can predict a patient's risk of developing C. difficile much earlier than it would be diagnosed with current methods. Preliminary data from their study, was recently published in Infection Control and Hospital Epidemiology.