Integrating phase-rectified signal averaging with machine learning to predict stroke-associated infections: a retrospective cohort study

Machine learning algorithms, when integrated with demographic and clinical factors, demonstrated accurate prediction of SAI in patients with AIS. The CAT model exhibited robust performance, highlighting its potential to enhance early detection and treatment in clinical practice. Additionally, PRSA markers may serve as potential targets for preventive interventions, enabling more judicious, timely, and targeted use of antibiotics. This approach opens new avenues for research into the prophylactic management of SAI.