TY - JOUR
T1 - Hierarchical Transformer Model with Uncertainty-Aware Attention for Predicting Delayed Cerebral Ischemia after Aneurysmal Subarachnoid Hemorrhage Using Hourly Neurological and Vital Sign Data
AU - John Miller
AU - Mark Davis
AU - Ethan Clark
JF - Journal of Artificial Intelligence for Healthcare Systems
JO - J. Artif. Intell. Healthc. Syst.
SN - 3149-8981
Y1 - 2026
VL - 5
IS - 2
SP - 138
N2 - Delayed cerebral ischemia (DCI) following aneurysmal subarachnoid hemorrhage is a significant cause of morbidity, mortality, and long-term neurological disability. Current clinical scores like WFNS, Hunt-Hess, and modified Fisher scale provide useful baseline risk information but often fail to capture subtle multi-hour deteriorations. Standard recurrent models can process sequential data but struggle with long-term dependencies and do not offer clinicians useful uncertainty information. To address this, a hierarchical Transformer model is proposed for DCI prediction, leveraging short-term hourly changes and longer multi-day trends in neurological and vital sign data. The model incorporates an uncertainty-aware attention mechanism to minimize the impact of unreliable or missing data and generates risk-stratified alerts with confidence levels. This approach aims to offer an explainable, clinically actionable tool that supports early recognition of DCI while ensuring clinician oversight. Future work will involve retrospective development and prospective validation to enhance its clinical utility.
UR - https://cirpublications.com/i719094821
ER -