Research progress on the application of learning algorithms in risk assessment of ICU-acquired infections in the intensive care unite

ICU-acquired infections is a significant challenge for critically ill patients in the Intensive Care Unit (ICU), and the early identification of infections and timely clinical interventions are crucial for improving patient outcomes. With the increasing prevalence of artificial intelligence (AI), machine learning has been widely applied in clinical practice, including disease diagnosis and prognostic risk assessment. This review aims to systematically summarize the research progress on ICU-acquired infection risk prediction models based on machine learning, in order to provide valuable evidence for clinical practice and references for future related studies.