Discovery of Generalizable TBI Phenotypes Using Multivariate Time-Series
  Clustering
Hamid Ghaderi Brandon Foreman Chandan K. Reddy Vignesh Subbian
Traumatic Brain Injury (TBI) presents a broad spectrum of clinical
presentations and outcomes due to its inherent heterogeneity, leading to
diverse recovery trajectories and varied therapeutic responses. While many
studies have delved into TBI phenotyping for distinct patient populations,
identifying TBI phenotypes that consistently generalize across various settings
and populations remains a critical research gap. Our research addresses this by
employing multivariate time-series clustering to unveil TBI's dynamic
intricates. Utilizing a self-supervised learning-based approach to clustering
multivariate time-Series data with missing values (SLAC-Time), we analyzed both
the research-centric TRACK-TBI and the real-world MIMIC-IV datasets.
Remarkably, the optimal hyperparameters of SLAC-Time and the ideal number of
clusters remained consistent across these datasets, underscoring SLAC-Time's
stability across heterogeneous datasets. Our analysis revealed three
generalizable TBI phenotypes ({\alpha}, \b{eta}, and {\gamma}), each exhibiting
distinct non-temporal features during emergency department visits, and temporal
feature profiles throughout ICU stays. Specifically, phenotype {\alpha}
represents mild TBI with a remarkably consistent clinical presentation. In
contrast, phenotype \b{eta} signifies severe TBI with diverse clinical
manifestations, and phenotype {\gamma} represents a moderate TBI profile in
terms of severity and clinical diversity. Age is a significant determinant of
TBI outcomes, with older cohorts recording higher mortality rates. Importantly,
while certain features varied by age, the core characteristics of TBI
manifestations tied to each phenotype remain consistent across diverse
populations.