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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2411.14637 |
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| _version_ | 1866914570017177600 |
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| author | Shi, Hanwen Zhang, Jin Zhang, Kunpeng |
| author_facet | Shi, Hanwen Zhang, Jin Zhang, Kunpeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_14637 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Clinical Trial Patient Matching through Knowledge Augmentation and Reasoning with Multi-Agent Shi, Hanwen Zhang, Jin Zhang, Kunpeng Multiagent Systems 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. |
| title | Enhancing Clinical Trial Patient Matching through Knowledge Augmentation and Reasoning with Multi-Agent |
| topic | Multiagent Systems |
| url | https://arxiv.org/abs/2411.14637 |