Hierarchical Neural Models for Temporal Relation Extraction in Clinical Narratives

Abstract

Hierarchical neural models for temporal relation extraction in clinical narratives have gained remarkable attention due to their capacity for capturing complex textual structures and contextual features across varying granularities of biomedical data. Clinical documents typically contain diverse references to events, which include symptom onset, therapeutic interventions, diagnostic measures, and disease progression. The ability to determine the precise temporal ordering of these events plays a crucial role in patient care, decision support, and retrospective analyses of disease trajectories. By leveraging hierarchical architectures, it becomes feasible to integrate multiple levels of representation, from word-level embeddings to document-level discourse patterns, in order to detect intricate temporal relationships among recorded clinical events. This paper aims to establish a new perspective on how to encode multi-scale contextual signals for robust recognition of temporal relations. It does so by examining formal modeling methodologies in conjunction with deep neural architectures that account for local syntactic cues and global narrative coherence. Our approach utilizes advanced methods to ensure comprehensive coverage of clinical text structures, coupled with suitable optimization strategies to maximize generalization performance in various clinical environments. Experimental results suggest that hierarchical neural frameworks provide clear advantages in the consistency, interpretability, and completeness of temporal relation extraction outputs. These findings lay a foundation for scalable deployment of automated temporal reasoning in emerging clinical applications.