Early Prediction of Delirium in ICU Patients Using Machine Learning Analysis of Admission Data

Delirium is a common ICU complication associated with increased morbidity, mortality, and healthcare costs. Early identification of at-risk patients can enable preventive interventions. In this study, we developed machine learning models to predict delirium onset using clinical data from the first 24 hours of ICU admission, extracted from the MIMIC-IV database. Adult patients with ICU stays longer than 24 hours and no delirium on day 1 were included. Delirium was defined as a positive CAM-ICU assessment between ICU days 2 and 7 in patients with RASS ≥ 3. Forty-two first-day clinical features were used to train SVM, random forest, and XGBoost models. Random forest and XGBoost achieved AUCs of 0.749 and 0.757, with first-day GCS and RASS identified as the most important predictors, highlighting the value of early ICU data for delirium risk prediction.