  Assessing acute brain dysfunction (ABD), including delirium and coma in the
intensive care unit (ICU), is a critical challenge due to its prevalence and
severe implications for patient outcomes. Current diagnostic methods rely on
infrequent clinical observations, which can only determine a patient's ABD
status after onset. Our research attempts to solve these problems by harnessing
Electronic Health Records (EHR) data to develop automated methods for ABD
prediction for patients in the ICU. Existing models solely predict a single
state (e.g., either delirium or coma), require at least 24 hours of observation
data to make predictions, do not dynamically predict fluctuating ABD conditions
during ICU stay (typically a one-time prediction), and use small sample size,
proprietary single-hospital datasets. Our research fills these gaps in the
existing literature by dynamically predicting delirium, coma, and mortality for
12-hour intervals throughout an ICU stay and validating on two public datasets.
Our research also introduces the concept of dynamically predicting critical
transitions from non-ABD to ABD and between different ABD states in real time,
which could be clinically more informative for the hospital staff. We compared
the predictive performance of two state-of-the-art neural network models, the
MAMBA selective state space model and the Longformer Transformer model. Using
the MAMBA model, we achieved a mean area under the receiving operator
characteristic curve (AUROC) of 0.95 on outcome prediction of ABD for 12-hour
intervals. The model achieves a mean AUROC of 0.79 when predicting transitions
between ABD states. Our study uses a curated dataset from the University of
Florida Health Shands Hospital for internal validation and two publicly
available datasets, MIMIC-IV and eICU, for external validation, demonstrating
robustness across ICU stays from 203 hospitals and 140,945 patients.
