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Mortality prediction in the ICU is essential for effective resource allocation and early clinical intervention. Bedside monitors generate multivariate physiological time-series data, but these are often irregularly sampled, making standard predictive modeling challenging. Conventional transformer models assume regular time intervals and fixed positional encodings, which limits their ability to capture clinically meaningful temporal gaps in real ICU data. This can reduce the accuracy of mortality risk prediction by obscuring important short- and long-term physiological patterns.To address this limitation, the TERTIAN framework introduces a hierarchical transformer with time-aware positional encoding specifically designed for irregular ICU data. It combines a local transformer to capture short-term dynamics and a global transformer to model long-term trends, integrating both through time-sensitive attention mechanisms. This design eliminates the need for data imputation and improves temporal understanding of patient trajectories.Overall, the framework better captures rapid deterioration and gradual decline by respecting irregular sampling patterns, leading to more clinically relevant predictions. By aligning model structure with real-world ICU data characteristics, TERTIAN offers a promising approach for improving mortality prediction and supporting critical care decision-making.

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.