Year: 2026
Type: conference-paper
Abstract: Clinical Concept Normalization is essential for clinical research applications involving trial protocols, such as patient-trial matching.Existing approaches focus heavily on specific domains and need large, annotated datasets.To address these challenges, we propose CENT, a context engineering framework that combines semantic matching for candidate retrieval and Large Language Model (LLM) prompting for disambiguation.CENT achieves superior performance on clinical procedures normalization in both binary and hierarchical metrics compared to semantic matching or LLM-only approaches, without requiring fine-tuning.Advanced prompt strategies, including Chain-of-Thought and Tree-of-Thoughts, achieve high performance at practical cost.CENT is scalable and adaptable for normalization across diverse clinical vocabularies in real-world clinical applications.
Institutions: Johnson & Johnson (United States), University of Washington
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
OpenAlex ID: W7166863781