In natural language processing applied to the clinical domain, utilizing
large language models has emerged as a promising avenue for error detection and
correction on clinical notes, a knowledge-intensive task for which annotated
data is scarce. This paper presents MedReAct'N'MedReFlex, which leverages a
suite of four LLM-based medical agents. The MedReAct agent initiates the
process by observing, analyzing, and taking action, generating trajectories to
guide the search to target a potential error in the clinical notes.
Subsequently, the MedEval agent employs five evaluators to assess the targeted
error and the proposed correction. In cases where 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. One core component of our method is our RAG
pipeline based on our ClinicalCorp corpora. Among other well-known sources
containing clinical guidelines and information, we preprocess and release the
open-source MedWiki dataset for clinical RAG application. Our results
demonstrate the central role of our RAG approach with ClinicalCorp leveraged
through the MedReAct'N'MedReFlex framework. It achieved the ninth rank on the
MEDIQA-CORR 2024 final leaderboard.

应用于临床领域的自然语言处理中，使用大型语言模型成为一种有前景的方法，用于在医疗笔记中进行错误检测和纠正，而这是一项缺乏注释数据的知识密集型任务。本文介绍了 MedReAct'N'MedReFlex，该系统利用四个基于 LLM 的医学代理。其中，MedReAct 代理通过观察、分析和采取行动，生成轨迹以指导搜索，从而定位潜在的错误。随后，MedEval 代理使用五个评估器评估目标错误和提出的修正方案。在 MedReAct 的行动不足以解决问题的情况下，MedReFlex 代理介入，进行反思分析并提出替代策略。最后，MedFinalParser 代理格式化最终输出，保留原始风格同时确保错误修正过程的完整性。我们方法的一个核心组成部分是基于 ClinicalCorp 语料库的 RAG 流水线。除了其他包含临床指南和信息的知名资源之外，我们对开源 MedWiki 临床数据集进行预处理和发布，用于临床 RAG 应用。我们的结果证明了我们的 RAG 方法及其通过 MedReAct'N'MedReFlex 框架的 ClinicalCorp 的核心作用，它在 MEDIQA-CORR 2024 最终排行榜上排名第九。