用双向GRU模型实现临床文本的自动医学概念标注
Automated SNOMED CT Concept Annotation in Clinical Text Using Bi-GRU Neural Networks
- 采用双向GRU模型进行序列标注，结合上下文特征提升识别精度
- 在MIMIC-IV子集上达到90%的F1分数，优于传统规则系统
- 轻量级架构适合实际部署，计算成本远低于变压器模型
临床文本的标准化概念标注对结构化数据提取和决策支持至关重要。SNOMED CT 提供了丰富的医学实体本体，但人工标注耗时且难以规模化。本研究提出一种基于双向GRU的神经序列标注方法，用于识别SNOMED CT概念。利用MIMIC-IV子集，通过领域适配的SpaCy与SciBERT分词预处理，将句子切分为重叠的19词块，并融入上下文、句法和形态特征。该模型对概念片段分配IOB标签，在验证集上取得90%的F1分数，优于传统规则系统，达到或超过现有神经模型表现。定性分析显示其能有效处理歧义术语与拼写错误。结果表明，轻量级RNN架构可在显著降低计算开销的前提下实现高质量临床概念标注，适用于真实场景部署。
原文摘要 · Abstract (English)
Automated annotation of clinical text with standardized medical concepts is critical for enabling structured data extraction and decision support. SNOMED CT provides a rich ontology for labeling clinical entities, but manual annotation is labor-intensive and impractical at scale. This study introduces a neural sequence labeling approach for SNOMED CT concept recognition using a Bidirectional GRU model. Leveraging a subset of MIMIC-IV, we preprocess text with domain-adapted SpaCy and SciBERT-based tokenization, segmenting sentences into overlapping 19-token chunks enriched with contextual, syntactic, and morphological features. The Bi-GRU model assigns IOB tags to identify concept spans and achieves strong performance with a 90 percent F1-score on the validation set. These results surpass traditional rule-based systems and match or exceed existing neural models. Qualitative analysis shows effective handling of ambiguous terms and misspellings. Our findings highlight that lightweight RNN-based architectures can deliver high-quality clinical concept annotation with significantly lower computational cost than transformer-based models, making them well-suited for real-world deployment.
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