Real-time monitoring of patient conditions in the ICU environment is essential
in supporting clinical decisions and ensuring optimal allocation of medical
resources. This study focuses on accurately predicting in-hospital ICU patient
mortality utilizing functional profile data. We demonstrate that a boosted
trees ensemble model is well suited for the diverse data typologies present in
ICU data and provides an interpretable and accurate model to aid clinical
experts in critical ICU decisions.