使用大型语言模型增强电子健康记录文本摘要
Enhanced Electronic Health Records Text Summarization Using Large Language Models
AI总结：
本项目提出基于Google Flan-T5模型并在SQuAD风格EHR问答数据集上微调的系统，生成临床医生偏好的聚焦EHR摘要，在EM、ROUGE和BLEU指标上表现优异，助力医疗数字化转型与个性化护理。
摘要：
电子健康记录（EHR）摘要系统的发展彻底改变了患者数据管理。先前的研究通过将大型语言模型（LLM）应用于临床任务，使用多样化的数据集生成通用的EHR摘要，从而推进了该领域。然而，临床医生通常需要特定的、聚焦的摘要以更快地获得洞察。本项目在先前工作的基础上，创建了一个生成临床医生偏好的聚焦摘要的系统，改进了EHR摘要以实现更高效的患者护理。所提出的系统利用Google Flan-T5模型，根据临床医生指定的主题生成定制的EHR摘要。该方法涉及在以Stanford Question Answering Dataset (SQuAD)风格格式化的EHR问答数据集上微调Flan-T5模型，SQuAD是一个包含问答的大规模阅读理解数据集。微调使用了Hugging Face Transformers库中的Seq2SeqTrainer，并优化了超参数。关键评估指标显示出有希望的结果：系统实现了81.81%的精确匹配（EM）得分。ROUGE（面向召回率的摘要评估研究）指标表现出强劲性能，ROUGE-1为96.03%，ROUGE-2为86.67%，ROUGE-L为96.10%。此外，双语评估替补（BLEU）得分为63%，反映了模型在生成摘要方面的连贯性。通过LLM增强EHR摘要，本项目支持医疗保健中的数字化转型，简化工作流程，并实现更个性化的患者护理。
英文摘要：
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.
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。
