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BACKGROUND: While the Glasgow coma scale (GCS) is one of the strongest outcome predictors, the current classification of traumatic brain injury (TBI) as 'mild', 'moderate' or 'severe' based on this fails to capture enormous heterogeneity in pathophysiology and treatment response. We hypothesized that data-driven characterization of TBI could identify distinct endotypes and give mechanistic insights. METHODS: We developed an unsupervised statistical clustering model based on a mixture of probabilistic graphs for presentation (

Original publication




Journal article


Crit Care

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Critical care, Endotypes, Intensive care unit, Machine learning, Traumatic brain injury, Unsupervised clustering, Brain Injuries, Traumatic, Cluster Analysis, Critical Care, Glasgow Coma Scale, Humans, Prognosis