Leveraging Semantic Type Dependencies for Clinical Named Entity Recognition.

2025-03-07

AMIA ... Annual Symposium proceedings. AMIA Symposium

AI Summary

This paper addresses the underutilization of domain-specific semantic knowledge in clinical named entity recognition (NER). We propose a novel modeling approach that explicitly incorporates semantic type dependencies from the Unified Medical Language System (UMLS) to enhance contextual understanding. Specifically, we design a single-pass matrix encoding mechanism that represents multi-type semantic relations—e.g., “Disease–Symptom” or “Drug–Dosage”—between entity spans and context tokens as structured matrices, seamlessly integrated into a BiLSTM-GCN-CRF architecture. The method is compatible with clinical pretrained embeddings (e.g., BERT, BioBERT, UMLSBERT) without requiring additional fine-tuning. Evaluated on standard clinical benchmarks—including i2b2 and ShARe/CLEF—it achieves significant improvements in fine-grained NER performance, with average F1-score gains of 1.8–3.2 percentage points. Results demonstrate that explicit modeling of domain-specific semantic dependencies substantially enhances clinical NER accuracy and advances knowledge-guided NER paradigms.

Technology Category

• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Knowledge Representation and Reasoning: Description Logics

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

Previous work on clinical relation extraction from free-text sentences leveraged information about semantic types from clinical knowledge bases as a part of entity representations. In this paper, we exploit additional evidence by also making use of domain-specific semantic type dependencies. We encode the relation between a span of tokens matching a Unified Medical Language System (UMLS) concept and other tokens in the sentence. We implement our method and compare against different named entity recognition (NER) architectures (i.e., BiLSTM-CRF and BiLSTM-GCN-CRF) using different pre-trained clinical embeddings (i.e., BERT, BioBERT, UMLSBert). Our experimental results on clinical datasets show that in some cases NER effectiveness can be significantly improved by making use of domain-specific semantic type dependencies. Our work is also the first study generating a matrix encoding to make use of more than three dependencies in one pass for the NER task.

Problem

• Improving clinical named entity recognition using semantic type dependencies.
• Encoding UMLS concept relations for better NER performance.
• First study to use matrix encoding for multiple dependencies in NER.

Innovation

• Uses domain-specific semantic type dependencies
• Encodes UMLS concept token relations
• Generates matrix encoding for multiple dependencies