Early sepsis prediction using time-series machine learning: A multi-dataset analysis and dynamic risk stratification

Abstract

<ns3:p>Sepsis is a critical, time-dependent syndrome, and its early detection is paramount for improving patient outcomes. This work validates a robust machine learning framework for early sepsis prediction by identifying key, dynamic biomarkers capable of providing accurate and clinically interpretable predictions. The research utilized two complementary critical care datasets: SepsisExp, which includes expert-validated labels, and MIMIC-III, the standard reference for intensive care research.</ns3:p>

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