Predicting the risk of early intensive care unit admission for patients hospitalized with acute pancreatitis using supervised machine learning

Background Acute pancreatitis (AP) is a complex and life-threatening disease. Early recognition of factors predicting morbidity and mortality is crucial. We aimed to develop and validate a pragmatic model to predict the individualized risk of early intensive care unit (ICU) admission for patients with AP.