用大模型自动提炼医患对话关键信息生成临床摘要
CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations
- 分两步提取：先筛出SOAP结构化信息，再用微调大模型生成摘要
- 在1473组医患对话上训练，自动评估和专家打分均优于现有方法
- 适合医疗自动化、电子病历生成等需要高效摘要的场景
本文提出ClinicSum框架，用于从医患对话中自动生成临床摘要。该框架采用双模块设计：检索过滤模块从对话记录中提取主观、客观、评估与计划（SOAP）信息；推理模块基于微调的预训练语言模型（PLM），利用提取的SOAP数据生成凝练的临床摘要。为训练PLM，研究者整合了两个公开数据集FigShare与MTS-Dialog，构建包含1,473组对话-摘要对的训练集，并由领域专家验证摘要质量。通过自动指标（如ROUGE、BERTScore）和专家人工评估双重验证，结果表明ClinicSum在自动评估中各项指标优于现有先进模型，在人类评估中也获得专家高度偏好，展现出在自动化临床摘要生成中的强大潜力。
原文摘要 · Abstract (English)
This paper presents ClinicSum, a novel framework designed to automatically generate clinical summaries from patient-doctor conversations. It utilizes a two-module architecture: a retrieval-based filtering module that extracts Subjective, Objective, Assessment, and Plan (SOAP) information from conversation transcripts, and an inference module powered by fine-tuned Pre-trained Language Models (PLMs), which leverage the extracted SOAP data to generate abstracted clinical summaries. To fine-tune the PLM, we created a training dataset of consisting 1,473 conversations-summaries pair by consolidating two publicly available datasets, FigShare and MTS-Dialog, with ground truth summaries validated by Subject Matter Experts (SMEs). ClinicSum's effectiveness is evaluated through both automatic metrics (e.g., ROUGE, BERTScore) and expert human assessments. Results show that ClinicSum outperforms state-of-the-art PLMs, demonstrating superior precision, recall, and F-1 scores in automatic evaluations and receiving high preference from SMEs in human assessment, making it a robust solution for automated clinical summarization.
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