The paper introduces a multi-agent framework called MedReAct'N'MedReFlex to address the task of medical error detection and correction in clinical notes. The framework integrates four distinct medical agents: MedReAct, MedReFlex, MedEval, and MedFinalParser, each playing a specialized role in the error identification and rectification process.
The MedReAct agent initiates the process by observing, analyzing, and taking action, generating trajectories to guide the search for potential errors in the clinical notes. The MedEval agents then employ five evaluators to assess the targeted error and the proposed correction. If MedReAct's actions prove insufficient, the MedReFlex agent intervenes, engaging in reflective analysis and proposing alternative strategies. Finally, the MedFinalParser agent formats the final output, preserving the original style while ensuring the integrity of the error correction process.
The authors leverage a Retrieval-Augmented Generation (RAG) framework based on MedRAG and MedCPT, operating over ClinicalCorp, a comprehensive corpus curated to encompass crucial clinical guidelines. Additionally, the authors introduce MedWiki, a collection of medical articles from Wikipedia, and provide the recipe to assemble the ClinicalCorp corpus.
The framework achieved the ninth rank in the MEDIQA-CORR 2024 competition, with an aggregation score of 0.581. The authors further optimize the framework, demonstrating substantial performance improvements by tuning the retrieval and reranking parameters, as well as the MedEval agent's evaluation thresholds.
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by Jean-Philipp... às arxiv.org 04-25-2024
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