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

TL;DR

This paper presents MAKAR, a multi-agent system that improves clinical trial patient matching through knowledge augmentation and reasoning, achieving better accuracy, privacy, and transparency.

Contribution

Introducing MAKAR, a novel multi-agent system that enhances patient-trial matching with criterion augmentation and structured reasoning, improving performance and privacy.

Findings

MAKAR improves matching performance by 7% on average.

It enables privacy-preserving deployment with open-source models.

MAKAR enhances transparency and accuracy in patient matching.

Abstract

Matching patients effectively and efficiently for clinical trials is a significant challenge due to the complexity and variability of patient profiles and trial criteria. This paper introduces \textbf{Multi-Agent for Knowledge Augmentation and Reasoning (MAKAR)}, a novel multi-agent system that enhances patient-trial matching by integrating criterion augmentation with structured reasoning. MAKAR consistently improves performance by an average of 7\% across different datasets. Furthermore, it enables privacy-preserving deployment and maintains competitive performance when using smaller open-source models. Overall, MAKAR can contributes to more transparent, accurate, and privacy-conscious AI-driven patient matching.