Head injuries are a leading global cause of mortality and disability, highlighting the critical need for advanced prognostic tools to inform clinical decision-making and optimize healthcare resource utilization. For the first time, this study introduces a cutting-edge artificial intelligence (AI) framework designed to predict mortality outcomes from head injury narratives. Leveraging deep learning-based natural language processing techniques, the framework identifies and extracts key features from unstructured text describing injury mechanisms and patient conditions to train predictive models. Validation was conducted on a diverse dataset of 1,500 head injury cases using a stratified holdout approach, with 90% allocated for training and 10% for testing. The one-dimensional convolutional neural network model demonstrated strong performance, achieving averagely 85% accuracy, 74% correct mortality prediction, 88% correct survival prediction, and an impressive area under the receiver operating characteristic curve of 0.91. This work highlights the transformative potential of AI in harnessing narrative clinical data to enhance prognostic accuracy, paving the way for more effective, evidence-based management of head injury patients.

The authors have declared no competing interest.

This study did not receive any funding.

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This study was approved by the Research Ethics Committee of Queen Mary University of London (QME25.0913).

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