A supervised machine learning model using artificial neural networks (ANN) predicted neurological recovery, including survival excellently, and outperformed a conventional model based on logistic regression. Among the data available at the time of hospitalisation, factors related to the pre-hospital setting carried most information.

ANN may be used to stratify a heterogenous trial population in risk classes and help determine intervention effects across subgroups.

The outcome was predicted with an area under the receiver operating characteristic curve (AUC) of 0.891 using 54 clinical variables available on admission to hospital, categorised as background, pre-hospital and admission data.

Corresponding models using background, pre-hospital or admission variables separately had inferior prediction performance.

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