Blood Transfusion

Blood transfusion, a critical medical procedure for managing anemia and coagulopathy, is undergoing a transformation driven by advanced machine learning. Current research focuses on developing robust predictive models, primarily using meta-learners and transformer-based architectures, to accurately assess the need for transfusions in diverse patient populations and improve resource allocation. These models leverage diverse data sources, including patient demographics and physiological parameters, to enhance prediction accuracy and identify key biomarkers. The ultimate goal is to optimize transfusion decisions, minimizing risks and improving patient outcomes while optimizing healthcare resource utilization.