Sepsis is one of the deadliest conditions in intensive care units (ICUs), triggered by the body’s out-of-control response to infection. Despite medical advancements, its in-hospital mortality rate still hovers between 20% and 50%. The challenge lies in early identification—sepsis is highly dynamic, and current scoring systems like APACHE-II and SOFA are not specifically designed to track its rapid progression. While machine learning has shown promise, most models struggle to account for real-time fluctuations in patient data. Given these challenges, an advanced predictive system capable of continuously learning from incoming clinical data is urgently needed to improve early detection and patient outcomes.
On February 8, 2025, researchers from Sichuan University, the University of A Coruña, and their…