Abstract
Despite advances in critical care, sepsis is a major source of patient suffering and mortality worldwide, with nearly 50 million cases and 11 million deaths each year. Early recognition is crucial to successful treatment of sepsis with anti-infectives, intravenous fluids, and circulatory support, and researchers have long sought to use artificial intelligence (AI) to identify patients showing early signs of the condition. But the development of AI-based sepsis prediction has been complex and hindered by shortcomings—eg, one algorithm, trained between 2008 and 2010, showed substantial deterioration in performance over time.
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