改进基于复杂医患对话的临床笔记生成
Improving Clinical Note Generation from Complex Doctor-Patient Conversation
AI总结：
该研究针对临床笔记手动撰写耗时的问题，推出CliniKnote数据集，提出带关键词的K-SOAP笔记格式，并开发自动化生成流水线，相较标准LLM微调方法显著提升了效率与性能。
摘要：
撰写临床笔记并记录医学检查是医疗保健专业人员的一项核心任务，也是患者护理文档的重要组成部分。然而，手动撰写这些笔记十分耗时，可能会减少临床医生可用于直接患者互动及其他任务的时间。因此，自动化临床笔记生成系统的开发已成为医疗人工智能领域中具有临床意义的研究方向。在本文中，我们针对利用大语言模型（LLM）的临床笔记生成领域提出了三项关键贡献。首先，我们推出了CliniKnote（临床笔记数据集），这是一个包含1200组复杂医患对话及其对应完整临床笔记的综合数据集。该数据集由医学专家在现代神经网络的协助下创建与整理，为临床笔记生成任务中的模型训练与评估提供了宝贵资源。其次，我们提出了K-SOAP（关键词、主观信息、客观信息、评估与计划）笔记格式，该格式在传统SOAP（主观、客观、评估、计划）笔记的基础上，在顶部新增了关键词部分，可帮助快速识别核心信息。第三，我们开发了一套从医患对话生成K-SOAP笔记的自动化流水线，并使用多种指标对多款现代LLM进行了基准测试。我们的结果表明，与标准的LLM微调方法相比，该方法在效率和性能上均实现了显著提升。
英文摘要：
Writing clinical notes and documenting medical exams is a critical task for healthcare professionals, serving as a vital component of patient care documentation. However, manually writing these notes is time-consuming and can impact the amount of time clinicians can spend on direct patient interaction and other tasks. Consequently, the development of automated clinical note generation systems has emerged as a clinically meaningful area of research within AI for health. In this paper, we present three key contributions to the field of clinical note generation using large language models (LLMs). First, we introduce CliniKnote, a comprehensive dataset consisting of 1,200 complex doctor-patient conversations paired with their full clinical notes. This dataset, created and curated by medical experts with the help of modern neural networks, provides a valuable resource for training and evaluating models in clinical note generation tasks. Second, we propose the K-SOAP (Keyword, Subjective, Objective, Assessment, and Plan) note format, which enhances traditional SOAP~\cite{podder2023soap} (Subjective, Objective, Assessment, and Plan) notes by adding a keyword section at the top, allowing for quick identification of essential information. Third, we develop an automatic pipeline to generate K-SOAP notes from doctor-patient conversations and benchmark various modern LLMs using various metrics. Our results demonstrate significant improvements in efficiency and performance compared to standard LLM finetuning methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
