CENT: Context Engineering Framework for Normalization of Clinical Trial Procedures

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Health & Wellbeing — Health & Medical Research, Clinical Care & Medical Practice, Medical Devices & Health Technology · Depth: Expert, medium

Summary

CENT, a context engineering framework, addresses challenges in Clinical Concept Normalization for clinical trial procedures, which typically require large, domain-specific annotated datasets. Developed by Sanya Taneja, Ziqing Ji, Hans Verstraete, and Ali Samadani, CENT integrates semantic matching for candidate retrieval with Large Language Model (LLM) prompting for disambiguation. Evaluated on a publicly available dataset of procedures normalized to Current Procedural Terminology (CPT) concepts, CENT demonstrated superior performance in both binary and hierarchical metrics compared to standalone semantic matching or LLM-only methods, notably without requiring fine-tuning. The framework utilizes advanced prompt strategies, including Chain-of-Thought and Tree-of-Thoughts, to achieve high performance efficiently. CENT was further validated on two clinical protocol-derived datasets containing noisy procedure texts, proving its scalability and adaptability for diverse clinical vocabularies in real-world applications. This work was presented at BioNLP 2026 in San Diego, California, spanning pages 729–741.

Key takeaway

For NLP Engineers or Research Scientists developing clinical concept normalization systems, CENT offers a robust, fine-tuning-free approach. If you are struggling with domain-specific data requirements or noisy clinical texts, consider integrating semantic matching with advanced LLM prompting strategies like Chain-of-Thought. This framework allows you to achieve superior normalization performance across diverse clinical vocabularies, enhancing applications like patient-trial matching, without the overhead of extensive dataset annotation or model retraining.

Key insights

CENT combines semantic matching and LLM prompting for robust, adaptable clinical concept normalization without fine-tuning.

Principles

• Context engineering enhances LLM disambiguation.
• Hierarchical metrics reveal deeper normalization accuracy.
• Advanced prompting reduces fine-tuning needs.

Method

CENT employs semantic matching for initial candidate retrieval, followed by Large Language Model (LLM) prompting, utilizing strategies like Chain-of-Thought or Tree-of-Thoughts, for precise disambiguation of clinical concepts.

In practice

• Normalize clinical procedures to CPT codes.
• Adapt to diverse clinical vocabularies.
• Process noisy clinical protocol texts.

Topics

• Clinical Concept Normalization
• Large Language Models
• Prompt Engineering
• Semantic Matching
• Clinical Trial Procedures
• CPT Codes

Best for: AI Scientist, NLP Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.