An Empirical Assessment of Machine Learning Models for Delirium Prediction Using the eICU-CRD v2.0 Database - de Souza, Márcio Wellington; Minatel, Diego (Anais Estendidos do Simpósio Brasileiro de Banco de Dados (SBBD); 2026: Anais Estendidos do XLI Simpósio Brasileiro de Bancos de DadosDO - 10.5753/sbbd_estendido.2026.249653, 2026)
Population aging intensifies the challenges faced by healthcare systems, particularly in managing conditions such as hospital-acquired delirium, a neuropsychiatric disorder characterized by acute onset and a fluctuating course, and associated with increased mortality and functional decline. Despite its clinical relevance, delirium frequently remains underdiagnosed during hospitalization. This paper investigates the use of machine learning to predict delirium early in intensive care units, using data from the eICU-CRD v2.0 database. A domain expert clinician selected the analyzed attributes, ensuring clinical coherence in the modeling process. We evaluated five classification algorithms and four data-balancing techniques using stratified cross-validation and metrics appropriate for imbalanced scenarios. Random Forest with Random Undersampling achieved the best overall performance. Our findings suggest that machine learning-based approaches can support earlier recognition of delirium and enhance clinical decision-making.