Enhancing Clinical Trial Patient Matching through Knowledge Augmentation and Reasoning with Multi-Agent

2026-05-19 · Source: cs.MA updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Health & Medical Research · Depth: Expert, long

Summary

A novel framework, Multi-Agents for Knowledge Augmentation (MAKA), significantly enhances patient matching for clinical trials by dynamically supplementing prompts with external, domain-specific knowledge. MAKA's architecture comprises five agents: a Knowledge Probing Agent to detect gaps, a Navigation Agent to manage specialized augmentation agents, a Knowledge Augmentation Agent to incorporate relevant information, a Supervision Agent to align outputs, and a Matching Agent for final selection. This approach addresses inherent knowledge gaps in both trial criteria and Large Language Models (LLMs), improving alignment between patient characteristics and eligibility criteria. Evaluated on the n2c2 2018 dataset, MAKA achieved an average accuracy of 0.909 and an F1-score of 0.828 at the criterion level, outperforming zero-shot and Chain-of-Thought (CoT) methods. At the trial level, MAKA also demonstrated superior performance with an accuracy of 0.9306 and an F1-score of 0.6552.

Key takeaway

For NLP Engineers developing clinical trial matching systems, MAKA demonstrates that augmenting LLMs with a structured multi-agent workflow can significantly improve accuracy and F1-score. You should consider implementing dynamic knowledge augmentation, especially for complex or ambiguous criteria, to enhance LLM understanding and reduce manual patient-filtering efforts. This approach can lead to more efficient and accurate patient recruitment.

Key insights

Multi-agent systems can augment LLMs with domain knowledge to improve clinical trial patient matching accuracy.

Principles

• Dynamic knowledge augmentation improves LLM performance.
• Multi-agent architectures enhance complex problem-solving.

Method

MAKA uses five specialized agents: Knowledge Probing, Navigation, Knowledge Augmentation, Supervision, and Matching. It identifies and fills knowledge gaps in clinical trial criteria, enriching prompts for LLM-based patient eligibility assessment.

In practice

• Augment LLM prompts with domain-specific knowledge.
• Use multi-agent systems for complex, knowledge-intensive tasks.

Topics

• Clinical Trial Patient Matching
• Multi-Agent Systems
• Large Language Models
• Knowledge Augmentation
• n2c2 Dataset

Best for: NLP Engineer, AI Scientist, Research Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.MA updates on arXiv.org.