AI-Powered Mortality Prognostication from Head Injury Narratives

Abstract

Head injuries are a major global cause of mortality and disability, underscoring the urgent need for accurate prognostication tools to guide clinical decision-making and optimize resource allocation. This study presents an artificial intelligence (AI)-powered framework for mortality prognostication derived from head injury narratives. Utilizing deep learning-based natural language processing techniques, the framework extracts critical features from unstructured text describing injury mechanisms and patient conditions to train a predictive model. Validation was conducted on a diverse dataset of 1,500 head injury cases using a non-stratified holdout partition, where 90% of observations formed the training set and 10% constituted the test set. The AI-based approach achieved an accuracy of 85%, sensitivity (correct prognostication of mortality) of 74%, specificity (correct prognostication of survival) of 86%, and an area under the receiver operating characteristic curve of 0.91. By harnessing the richness of narrative data, this study demonstrates the potential of AI to significantly enhance prognostication accuracy and improve evidence-based management of head injury patients.Copyright © 2025