In this work, we present a manually annotated corpus for Adverse Event (AE) extraction from discharge summaries of elderly patients, a population often underrepresented in clinical NLP resources. The dataset includes 14 clinically significant AEs-such as falls, delirium, and intracranial haemorrhage, along with contextual attributes like negation, diagnosis type, and in-hospital occurrence. Uniquely, the annotation schema supports both discontinuous and overlapping entities, addressing challenges rarely tackled in prior work. We evaluate multiple models using FlairNLP across three annotation granularities: fine-grained, coarse-grained, and coarse-grained with negation. While transformer-based models (e.g., BERT-cased) achieve strong performance on document-level coarse-grained extraction (F1 = 0.943), performance drops notably for fine-grained entity-level tasks (e.g., F1 = 0.675), particularly for rare events and complex attributes. These results demonstrate that despite high-level scores, significant challenges remain in detecting underrepresented AEs and capturing nuanced clinical language. Developed within a Trusted Research Environment (TRE), the dataset is available upon request via DataLoch and serves as a robust benchmark for evaluating AE extraction methods and supporting future cross-dataset generalisation.

Haemorrhaging of the brain is the leading cause of death in people between the ages of 15 and 24 and the third leading cause of death in people older than that. Computed tomography (CT) is an imaging modality used to diagnose neurological emergencies, including stroke and traumatic brain injury. Recent advances in Deep Learning and Image Processing have utilised different modalities like CT scans to help automate the detection and segmentation of brain haemorrhage occurrences. In this paper, we propose a novel implementation of an architecture consisting of traditional Convolutional Neural Networks(CNN) along with Graph Neural Networks(GNN) to produce a holistic model for the task of brain haemorrhage segmentation.GNNs work on the principle of neighbourhood aggregation thus providing a reliable estimate of global structures present in images. GNNs work with few layers thus in turn requiring fewer parameters to work with. We were able to achieve a dice coefficient score of around 0.81 with limited data with our implementation.

Head CT is used worldwide to assess neurological emergencies and detect acute brain haemorrhages. Interpreting these head CT scans requires readers to identify tiny subtle abnormalities, with near-perfect sensitivity, within a 3D stack of greyscale images characterized by poor soft-tissue contrast, low signal-to-noise ratio and a high incidence of artefacts. As such, even highly trained experts may miss subtle life-threatening findings. To increase the efficiency, and potentially also the accuracy, of such image analysis, scientists at UC San Francisco (UCSF) and UC Berkeley have developed a fully convolutional neural network, called PatchFCN, that can identify abnormalities in head CT scans with comparable accuracy to highly trained radiologists. Importantly, the algorithm also localizes the abnormalities within the brain, enabling physicians to examine them more closely and determine the required therapy (PNAS 10.1073/pnas.1908021116).