Temporal Relation Extraction in Clinical Texts: A Span-based Graph Transformer Approach
This work addresses temporal relation extraction in clinical texts, which is crucial for improving diagnostic and prognostic models in healthcare, though it appears incremental as it builds on existing methods and datasets.
The paper tackled the problem of extracting clinical events and their temporal relations from text, achieving a 5.5% improvement in F1 score over the previous state-of-the-art on the I2B2 2012 corpus and up to 8.9% improvement on long-range relations.
Temporal information extraction from unstructured text is essential for contextualizing events and deriving actionable insights, particularly in the medical domain. We address the task of extracting clinical events and their temporal relations using the well-studied I2B2 2012 Temporal Relations Challenge corpus. This task is inherently challenging due to complex clinical language, long documents, and sparse annotations. We introduce GRAPHTREX, a novel method integrating span-based entity-relation extraction, clinical large pre-trained language models (LPLMs), and Heterogeneous Graph Transformers (HGT) to capture local and global dependencies. Our HGT component facilitates information propagation across the document through innovative global landmarks that bridge distant entities. Our method improves the state-of-the-art with 5.5% improvement in the tempeval $F_1$ score over the previous best and up to 8.9% improvement on long-range relations, which presents a formidable challenge. We further demonstrate generalizability by establishing a strong baseline on the E3C corpus. This work not only advances temporal information extraction but also lays the groundwork for improved diagnostic and prognostic models through enhanced temporal reasoning.