Traumatic brain injury is the leading cause of death and disability worldwide. Despite this large impact, no predictive models are in widespread use due to tedious data collection requirements, lack of provider trust, and poor performance. Furthermore, these models use simple, often binary, data elements that fail to capture the complex heterogeneity of traumatic brain injury. Recent advances in computational modeling efforts have demonstrated promising results for capturing imaging, clinical, electroencephalographic, and other biomarkers for powerful predictive models. In this review, we provide an overview of efforts in computational modeling in neurotrauma and provide insights into future directions.
Keywords: Artificial intelligence; Imaging; Machine learning; Recovery; Traumatic brain injury.
© 2024. The Author(s), under exclusive license to Springer Nature Switzerland AG.