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

[2411.14637] Enhancing Clinical Trial Patient Matching through Knowledge Augmentation and Reasoning with Multi-Agent Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Multiagent Systems arXiv:2411.14637 (cs) [Submitted on 22 Nov 2024 ( v1 ), last revised 15 May 2026 (this version, v5)] Title: Enhancing Clinical Trial Patient Matching through Knowledge Augmentation and Reasoning with Multi-Agent Authors: Hanwen Shi , Jin Zhang , Kunpeng Zhang View a PDF of the paper titled Enhancing Clinical Trial Patient Matching through Knowledge Augmentation and Reasoning with Multi-Agent, by Hanwen Shi and 1 other authors View PDF HTML (experimental) 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. Subjects: Multiagent Systems (cs.MA) Cite as: arXiv:2411.14637 [cs.MA] (or arXiv:2411.14637v5 [cs.MA] for this version) https://doi.org/10.48550/arXiv.2411.14637 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Hanwen Shi [ view email ] [v1] Fri, 22 Nov 2024 00:07:36 UTC (7,088 KB) [v2] Tue, 11 Feb 2025 21:58:06 UTC (260 KB) [v3] Sat, 5 Jul 2025 02:59:11 UTC (391 KB) [v4] Thu, 9 Apr 2026 00:24:00 UTC (378 KB) [v5] Fri, 15 May 2026 20:35:10 UTC (378 KB) Full-text links: Access Paper: View a PDF of the paper titled Enhancing Clinical Trial Patient Matching through Knowledge Augmentation and Reasoning with Multi-Agent, by Hanwen Shi and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.MA < prev | next > new | recent | 2024-11 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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