Enhancing Clinical Trial Patient Matching through Knowledge Augmentation and Reasoning with Multi-Agent
This addresses the problem of inefficient patient-trial matching for healthcare and clinical research, with incremental improvements in accuracy and privacy.
The paper tackled the challenge of matching patients to clinical trials by introducing MAKAR, a multi-agent system that integrates knowledge augmentation and reasoning, resulting in an average performance improvement of 7% across datasets.
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.