LAUSR

Key Points Question Can machine learning be used to predict incident delirium in newly hospitalized patients using only data available in the electronic health record shortly after admission? Findings In this cohort study, classification models were trained using 5 different machine learning algorithms on 14 227 hospital stays and validated on a prospective test set of 3996 hospital stays. The gradient boosting machine model performed best, with an area under the receiver operating characteristic curve of 0.855. Meaning Machine learning can accurately predict delirium risk using electronic health record data on admission and outperforms the nurse-administered prediction rules currently used.