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Transformer-based architectures have significantly advanced clinical natural language processing by improving the capture of contextual relationships in unstructured electronic health records compared to earlier recurrent and convolutional models, with domain-specific variants such as ClinicalBERT and BioBERT designed to better handle clinical terminology, abbreviations, and specialized language, thereby improving information extraction performance, although the relative impact of different pre-training strategies remains insufficiently synthesized and requires systematic evaluation of corpus selection and fine-tuning approaches; this systematic review mapped studies focusing on pre-training corpora, fine-tuning methods, and named entity recognition performance across entity types such as medications, diseases, procedures, laboratory tests, and social determinants of health, using PRISMA-guided methods and searches across PubMed, ACL Anthology, arXiv, and IEEE Xplore, identifying 32 eligible studies from 1,247 records; findings showed that ClinicalBERT, BioBERT, and PubMedBERT were the most frequently evaluated models, pre-trained on datasets such as MIMIC-III, PubMed abstracts, and mixed biomedical corpora, with consistent evidence that domain-specific pre-training outperforms general-domain BERT models on benchmarks like i2b2 and n2c2 despite variation across entity types and fine-tuning strategies, while clinical pre-training on large EHR corpora improves named entity recognition and optimized fine-tuning approaches such as lower learning rates and data augmentation further enhance performance, particularly for medications and diseases, underscoring the importance of domain adaptation and the need for more standardized evaluation protocols in clinical NLP research.

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.

Laboratory turnaround time is a core operational metric linking laboratory performance to clinical decision-making. Delays arise from interacting pre-analytical, analytical, and post-analytical factors that vary by order priority, specimen pathway, workload, and analyzer status. Most turnaround time monitoring remains retrospective, aggregated, and focused on average performance. Such monitoring does not estimate whether an individual test order is likely to become delayed under current operational conditions. This manuscript develops a conceptual transformer-based prediction model for estimating the probability of turnaround delay for each incoming laboratory test order. The model uses order priority, specimen collection time, transport route, department workload, analyzer availability, and historical processing patterns. A transformer encoder ingests timestamped processing milestones from the laboratory workflow, including order entry, specimen collection, transport, accessioning, analyzer loading, result generation, and verification. Static features such as order priority, test type, and transport route are fused with dynamic sequence features through attention-based integration. Conceptually, the model could identify orders at elevated risk of delay as soon as they enter the pre-analytical or analytical pipeline. Its predictions would be expected to support proactive prioritisation, specimen routing, workload balancing, and analyzer assignment. A transformer-based turnaround delay model could shift laboratory operations from retrospective monitoring toward proactive delay prevention. Prospective implementation studies would be needed to evaluate safety, reliability, workflow fit, and operational usefulness.