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Acute ischemic stroke prediction from electronic health record time series data holds significant potential for enabling early intervention and reducing long-term disability. LSTMs have been widely used to model clinical sequences such as vital signs and laboratory trends, showing strong performance in stroke-related prediction tasks from 2018–2022. However, their sequential nature limits scalability and long-range dependency modeling in large EHR datasets. Transformers, despite transforming sequence modeling in other domains since 2017, remain underused in stroke prediction compared to LSTMs. Although early healthcare studies suggest potential benefits of attention-based models, robust validation in acute ischemic stroke contexts is still limited. Transformers offer advantages in parallel processing, long-range dependency modeling, and interpretability, but require more data and computational resources. They are likely to complement rather than replace LSTMs, with hybrid architectures providing a balanced solution for clinical time series analysis. Key themes include long-range dependency capture, parallel computation, interpretability, and data efficiency trade-offs between LSTMs and transformers. Hybrid LSTM–transformer models may offer improved performance and practicality for stroke prediction, with model selection depending on data scale and clinical constraints. Further benchmarking is needed to determine when transformers or hybrid models outperform LSTMs, guiding the development of more effective stroke prediction systems.

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.