用时间感知注意力建模医疗记录的时间间隔，提升疾病预测准确率。
Time-Aware Attention for Enhanced Electronic Health Records Modeling
- 引入连续时间间隔的注意力机制，捕捉医疗事件间的动态变化
- 在MIMIC-IV和PIC数据集上疾病进展预测表现优于现有方法
- 结合大模型语义嵌入，增强临床概念理解，适合医疗AI研究者
电子健康记录（EHR）包含对预测患者结局和指导医疗决策至关重要的临床信息。然而，有效建模EHR需应对数据异质性和复杂的时序模式，传统方法常难以处理临床事件间的不规则时间间隔。本文提出TALE-EHR，一种基于Transformer的框架，包含新颖的时间感知注意力机制，能显式建模连续时间间隔，以捕捉精细的序列动态。为增强语义表征，TALE-EHR利用预训练大语言模型（LLM）从标准化代码描述中提取嵌入，构建强大的临床概念理解基础。在MIMIC-IV和PIC数据集上的实验表明，该方法在疾病进展预测等任务上优于当前最优基线。结果证明，将显式的连续时间建模与强语义表示相结合，可为推进EHR分析提供有效解决方案。
原文摘要 · Abstract (English)
Electronic Health Records (EHR) contain valuable clinical information for predicting patient outcomes and guiding healthcare decisions. However, effectively modeling Electronic Health Records (EHRs) requires addressing data heterogeneity and complex temporal patterns. Standard approaches often struggle with irregular time intervals between clinical events. We propose TALE-EHR, a Transformer-based framework featuring a novel time-aware attention mechanism that explicitly models continuous temporal gaps to capture fine-grained sequence dynamics. To complement this temporal modeling with robust semantics, TALE-EHR leverages embeddings derived from standardized code descriptions using a pre-trained Large Language Model (LLM), providing a strong foundation for understanding clinical concepts. Experiments on the MIMIC-IV and PIC dataset demonstrate that our approach outperforms state-of-the-art baselines on tasks such as disease progression forecasting. TALE-EHR underscores the benefit of integrating explicit, continuous temporal modeling with strong semantic representations provides a powerful solution for advancing EHR analysis.
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