用图注意力模型提升医疗文本中事件时序关系抽取效果
Temporal Relation Extraction in Clinical Texts: A Span-based Graph Transformer Approach
- 基于实体跨度和异构图变换器建模事件与时间关系
- 在I2B2数据集上F1提升5.5%，长距离关系最高提升8.9%
- 适合需要精准时序推理的临床决策系统研究者
从非结构化文本中提取时间信息对理解医疗事件至关重要。本文针对I2B2 2012时序关系挑战数据集，提出GRAPHTREX方法，结合基于跨度的实体-关系抽取、临床领域预训练语言模型（LPLMs）与异构图变压器（HGT），捕捉局部与全局依赖关系。其创新的全局地标机制促进文档内远距离实体间的信息传播。实验显示，该方法在tempeval F1上相比先前最佳模型提升5.5%，在长程关系上最高提升8.9%。此外，在E3C数据集上建立了强基线，验证了方法泛化能力。本工作不仅推进了时序信息抽取技术，也为增强诊断与预后模型的时序推理能力奠定基础。
原文摘要 · Abstract (English)
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.
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