Automated SNOMED CT Concept Annotation in Clinical Text Using Bi-GRU Neural Networks

TL;DR

This paper presents a neural network approach using Bi-GRU for automated SNOMED CT concept annotation in clinical text, achieving high accuracy with lower computational cost than transformer models.

Contribution

Introduces a Bi-GRU based method for SNOMED CT concept recognition in clinical text, demonstrating high performance and efficiency over existing models.

Findings

Achieved 90% F1-score on validation data.

Outperformed traditional rule-based systems.

Matched or exceeded other neural models in accuracy.

Abstract

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.