The field of clinical text analysis is witnessing significant advancements, driven by the development of innovative architectures and techniques. A key direction in this field is the integration of graph-based methods and transformer architectures to improve the extraction of temporal relations and clinical events from unstructured text. This has led to enhanced performance in tasks such as temporal information extraction, with improvements in state-of-the-art metrics. Additionally, there is a growing emphasis on interpretability and explainability in clinical predictive modeling, with novel mechanisms being proposed to capture dynamic interactions among clinical features across time. Noteworthy papers in this area include:

• Temporal Relation Extraction in Clinical Texts: A Span-based Graph Transformer Approach, which introduces a novel method integrating span-based entity-relation extraction and Heterogeneous Graph Transformers to capture local and global dependencies.
• No Black Box Anymore: Demystifying Clinical Predictive Modeling with Temporal-Feature Cross Attention Mechanism, which proposes a novel deep learning framework to capture dynamic interactions among clinical features across time, enhancing both predictive accuracy and interpretability.