An Agitation Sedation Level Prediction Model for ICU Patients

Abstract

Background: Intensive care units (ICUs) are crucial in healthcare, but internal factors, like patient agitation due to delirium, treatment discomfort, and the ICU environment, can compromise care and lead to safety issues. In Taiwan, the Richmond Agitation–Sedation Scale (RASS) is used for agitation assessment, but it has limitations, including subjectivity and infrequent assessments. Methods: To enhance ICU care, we developed a machine learning-based patient agitation and sedation assessment tool. We used an ensemble learning model, combining two machine learning models to classify patients into three categories: oversedation, optimal sedation, and agitation. Results: The RandomForest model achieved the highest average accuracy for sedation (ACC = 0.92, AUC = 0.97), while addressing class imbalance increased agitation classification accuracy (ACC = 0.77, AUC = 0.88). The model's results, based on key features identified, can guide sedative dosage adjustments, enabling more precise patient care. Conclusions: Our study demonstrated the effectiveness of machine learning in classifying patient agitation and sedation. We recommend incorporating image-based features in patient agitation assessment. Our classification system can assist medical professionals in RASS assessments, mitigating safety risks related to patient agitation in ICUs and improving overall ICU capacity. Trial Registration: We obtained access to the critical care database (AI-111010) of the AI Center of Taichung Veterans General Hospital (TCVGH) from the Institutional Review Board (approval number: CE22484A), retrospectively registered.