Stratifying Traumatic Brain Injury Mortality and Readmission Risk: An Unsupervised Machine Learning Approach

Background: Traumatic brain injury (TBI) presents several challenges due to its heterogeneity in care trajectories. Many who survive the acute phase remain at risk for long-term disability, hospital readmission, and mortality. Predicting these outcomes, particularly for those with severe injuries requiring intensive care, involves a complex interplay of clinical and patient-specific factors. This study used unsupervised machine learning (ML) to identify patient subgroups with clinically meaningful mortality and readmission risk profiles.