Computers in biology and medicine
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Comparative Study Clinical Trial
Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs.
Sepsis remains a costly and prevalent syndrome in hospitals; however, machine learning systems can increase timely sepsis detection using electronic health records. This study validates a gradient boosted ensemble machine learning tool for sepsis detection and prediction, and compares its performance to existing methods. ⋯ The MLA predicts sepsis up to 48 h in advance and identifies sepsis onset more accurately than commonly used tools, maintaining high performance for sepsis detection when trained and tested on separate datasets.