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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

DOI

10.1186/s13054-022-04079-w

Type

Journal article

Journal

Crit Care

Publication Date

27/07/2022

Volume

26

Keywords

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