  Baseline injury categorization is important to traumatic brain injury (TBI)
research and treatment. Current categorization is dominated by symptom-based
scores that insufficiently capture injury heterogeneity. In this work, we apply
unsupervised clustering to identify novel TBI phenotypes. Our approach uses a
generalized low-rank model (GLRM) model for feature selection in a procedure
analogous to wrapper methods. The resulting clusters reveal four novel TBI
phenotypes with distinct feature profiles and that correlate to 90-day
functional and cognitive status.
