  Determining clinically relevant physiological states from multivariate time
series data with missing values is essential for providing appropriate
treatment for acute conditions such as Traumatic Brain Injury (TBI),
respiratory failure, and heart failure. Utilizing non-temporal clustering or
data imputation and aggregation techniques may lead to loss of valuable
information and biased analyses. In our study, we apply the SLAC-Time
algorithm, an innovative self-supervision-based approach that maintains data
integrity by avoiding imputation or aggregation, offering a more useful
representation of acute patient states. By using SLAC-Time to cluster data in a
large research dataset, we identified three distinct TBI physiological states
and their specific feature profiles. We employed various clustering evaluation
metrics and incorporated input from a clinical domain expert to validate and
interpret the identified physiological states. Further, we discovered how
specific clinical events and interventions can influence patient states and
state transitions.
