文献类型：期刊文献

中文题名：基于大小模型结合与迭代反思框架的电子病历摘要生成方法

英文题名：Collaboration of Large and Small Language Models with Iterative Reflection Framework for Clinical Note Summarization

作者：钟博洋[1];阮彤[1];张维彦[1];刘井平[1]

机构：[1]华东理工大学信息工程与科学学院,上海200237

年份：2025

卷号：52

期号：9

起止页码：294

中文期刊名：计算机科学

外文期刊名：Computer Science

收录：;北大核心:【北大核心2023】；

语种：中文

中文关键词：大规模语言模型;医疗预训练模型;摘要生成;大模型反思;大小模型结合

外文关键词：Large language model;Medical pre-trained model;Summarization generation;Large model reflection;Collaboration of large and small models

摘要：在医疗人工智能领域,从医患对话中自动生成电子病历(EMR)是一项核心任务。传统主流方法多依赖于大规模语言模型(LLM)结合少量示例进行学习,然而,这些方法往往未能有效融入深度的医学专业知识,导致生成的EMR内容在专业性方面存在不足。针对这一挑战,提出了一种新颖的迭代反思框架,该框架融合了Error2Correct示例学习与领域模型监督,旨在提升EMR的总结质量。具体而言,首先设计了一种集成了Error2Correct示例学习机制的大规模语言模型,用于EMR的初步生成与持续优化,并在预生成阶段融入医学领域知识。然后利用一个经过微调的小规模医学预训练语言模型,对初步生成的EMR进行进一步的评估与优化,从而在后生成阶段再次深化领域知识的整合。最后,引入了一个迭代调度器,该调度器能够高效地引导模型在持续的反思与迭代过程中进行优化。实验结果显示,所提方法在两个公开的EMR数据集上均展现出了先进的性能。特别是在IMCS-V2-MRG和ACI-BENCH数据集上,与经过微调的大规模语言模型相比,所提方法分别实现了3.66个百分点和7.75个百分点的整体性能提升1)。
Generating clinical notes from doctor-patient dialogues is a critical task in medical artificial intelligence.Existing me-thods typically rely on large language models(LLMs)with few-shot demonstrations but often struggle to integrate sufficient domain-specific knowledge,leading to suboptimal and less professional outputs.To address this problem,a novel iterative reflection framework is proposed,which integrates Error2Correct example learning and domain-model supervision,aiming to improve the summary quality of EMR.Specifically,a large-cale language model integrating the Error2Correct example learning mechanism is designed for the initial generation and continuous potimization of EMR,and the medical domain knowledge is integrated into the pre-generation stage.Then,this paper uses a lightweight medical pre-training language model,fine-tuned with domain data,to evaluate the refined content,integrating domain knowledge in post-generation.Finally,an iterative scheduler is introduced,which can effectively guide the model to optimize in the continuous process of reflection and improvement.Experimental results on two public datasets demonstrate that the proposed method achieves state-of-the-art performance.Compared with the fine-tuned large language models,the proposed method improves overall performance by 3.68%and 7.75%on IMCS-V2-MRG and ACI-BENCH datasets.