用大模型生成医生关注的精准病历摘要，提升诊疗效率。
Enhanced Electronic Health Records Text Summarization Using Large Language Models
- 基于医生指定主题，微调Flan-T5生成聚焦式病历摘要。
- 在问答数据集上训练，达到81.81%精确匹配率，ROUGE-L达96.10%。
- 适合临床医生快速获取关键信息，助力智能病历系统建设。
电子健康记录（EHR）摘要系统的发展革新了患者数据管理方式。以往研究通过适配大语言模型于临床任务，利用多样数据集生成通用性病历摘要。然而，临床医生常需针对特定主题的聚焦摘要以快速获取洞察。本项目在此基础上构建了一个生成医生偏好型聚焦摘要的系统，提升了病历摘要的实用性与效率。系统采用Google Flan-T5模型，根据医生指定的主题生成定制化摘要。方法包括在斯坦福问答数据集（SQuAD）风格的EHR问答数据集上微调该模型，使用Hugging Face Transformers库中的Seq2SeqTrainer，并优化超参数。评估结果显示：精确匹配（EM）得分为81.81%；ROUGE指标表现优异，其中ROUGE-1为96.03%，ROUGE-2为86.67%，ROUGE-L为96.10%；同时BLEU得分为63%，表明生成摘要具有高连贯性。该研究通过大模型增强病历摘要能力，支持医疗数字化转型，优化工作流程，实现更个性化的患者照护。
原文摘要 · Abstract (English)
The development of Electronic Health Records summarization systems has revolutionized patient data management. Previous research advanced this field by adapting Large Language Models for clinical tasks, using diverse datasets to generate general EHR summaries. However, clinicians often require specific, focused summaries for quicker insights. This project builds on prior work by creating a system that generates clinician-preferred, focused summaries, improving EHR summarization for more efficient patient care. The proposed system leverages the Google Flan-T5 model to generate tailored EHR summaries based on clinician-specified topics. The approach involved fine-tuning the Flan-T5 model on an EHR question-answering dataset formatted in the Stanford Question Answering Dataset (SQuAD) style, which is a large-scale reading comprehension dataset with questions and answers. Fine-tuning utilized the Seq2SeqTrainer from the Hugging Face Transformers library with optimized hyperparameters. Key evaluation metrics demonstrated promising results: the system achieved an Exact Match (EM) score of 81.81%. ROUGE (Recall-Oriented Understudy for Gisting Evaluation) metrics showed strong performance, with ROUGE-1 at 96.03%, ROUGE-2 at 86.67%, and ROUGE-L at 96.10%. Additionally, the Bilingual Evaluation Understudy (BLEU) score was 63%, reflecting the model's coherence in generating summaries. By enhancing EHR summarization through LLMs, this project supports digital transformation efforts in healthcare, streamlining workflows, and enabling more personalized patient care.
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