Multifactorial Random Forest models using quantitative electroencephalography (qEEG) features, clinical data, and radiological findings have potential to predict neurological outcome in patients with moderate to severe traumatic brain injury (TBI).

57 patients with moderate to severe TBI were included and divided into a training set (n = 38) and a validation set (n = 19).

Our best model included eight qEEG parameters and MAP at 72 and 96 h after TBI, age, and nine other IMPACT parameters.

Continuous EEG measurements were performed during the first 7 days of ICU admission.

Patient outcome at 12 months was dichotomized based on the Extended Glasgow Outcome Score (GOSE) as poor (GOSE 1–2) or good (GOSE 3–8).

23 qEEG features were extracted.

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