Research in the news: Big data model improves prediction of key hospital outcome

By Ziba Kashef February 17, 2016 More than half of hospital deaths in the United States are related to severe infections, or sepsis. Yale researchers developed a prediction model, drawing on “big data” about local patients and using machine-learning methods, that proved better at identifying at-risk patients than existing clinical practices. Currently, emergency physicians can use simple calculators or point- scoring systems known as clinical decision rules to determine which hospitalized patients might die from sepsis. However, these methods often fail to identify patients most at risk because they are based on limited information, derived from models that are unable to capture the complexity of data, and developed using different patient populations. The new model developed by researchers at Yale School of Medicine and the University of Washington uses a…


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