Abstract
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.
Abstract (translated)
在将自然语言处理应用于临床领域时，利用大型语言模型在临床笔记中检测和纠正错误是一个有前景的途径。由于注释数据有限，这是一个知识密集型任务。本文介绍了一种名为MedReAct'N'MedReFlex的方法，它利用了一组基于LLM的医疗代理。MedReAct代理通过观察、分析和采取行动来启动过程，生成轨迹以指导搜索以定位临床笔记中的潜在错误。然后，MedEval代理采用五个评估者来评估所针对的错误及其提出的纠正措施。在MedReAct代理的行动证明不够充分的情况下，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，同时确保错误纠正过程的完整性。我们方法的一个核心组成部分是我们的RAG管道，基于我们的临床Corp语料库。与其他包含临床指南和信息的知名来源相比，我们预处理并发布了用于临床RAG应用的开放式源码MedWiki数据集。我们的结果表明，通过MedReAct'N'MedReFlex框架，我们RAG方法的临床Corp优势得到了充分发挥。它在地MEDIQA-CORR 2024最终排行榜上获得了第九名。
URL
https://arxiv.org/abs/2404.15488
