  Although prediction models for delirium, a commonly occurring condition
during general hospitalization or post-surgery, have not gained huge
popularity, their algorithmic bias evaluation is crucial due to the existing
association between social determinants of health and delirium risk. In this
context, using MIMIC-III and another academic hospital dataset, we present some
initial experimental evidence showing how sociodemographic features such as sex
and race can impact the model performance across subgroups. With this work, our
intent is to initiate a discussion about the intersectionality effects of old
age, race and socioeconomic factors on the early-stage detection and prevention
of delirium using ML.
