Agitation Sedation Monitoring System for Intensive Care Unit Based on Ensemble Learning Model

Intensive care units (ICUs) are a crucial part of the health-care system. Patients in ICUs may experience agitation due to delirium, treatment discomfort, and environment-related reasons. This may lead to various clinical safety problems, such as the self-removal of catheters and assault of nursing personnel by patients. Hospitals in Taiwan generally use the Richmond Agitation–Sedation Scale (RASS) to assess patient agitation and adjust sedative dosages. However, the RASS has certain limitations, such as the requirement of subjective assessment, differences in patient evaluation standards among medical personnel, and low frequency of assessment. These limitations may result in the insufficient or excessive sedation of patients. Therefore, in this study, we developed an ensemble learning model by combining two machine learning models. The proposed model classified patients into oversedation, maintain range, and agitation categories. The proposed classification system may effectively assist medical personnel in performing RASS assessment, mitigate patient agitation–related clinical safety risks in ICUs, and increase the medical capacity of ICUs.