Abstract
The Achilles heel of Large Language Models (LLMs) is hallucination, which has drastic consequences for the clinical domain. This is particularly important with regards to automatically generating discharge summaries (a lengthy medical document that summarizes a hospital in-patient visit). Automatically generating these summaries would free physicians to care for patients and reduce documentation burden. The goal of this work is to discover new methods that combine language-based graphs and deep learning models to address provenance of content and trustworthiness in automatic summarization. Our method shows impressive reliability results on the publicly available Medical Information Mart for Intensive III (MIMIC-III) corpus and clinical notes written by physicians at Anonymous Hospital. rovide our method, generated discharge ary output examples, source code and trained models.
Abstract (translated)
大型语言模型（LLMs）的阿基里斯之踵是幻觉现象，这对临床领域有着严重的后果。特别是在自动生成出院总结这一任务中尤为关键（这是一个详细的医疗文件，汇总了住院患者的访问记录）。如果能够实现自动生成这些摘要，医生就可以有更多时间照顾患者，并减少文档负担。本研究的目标是发现新的方法，结合语言图和深度学习模型来解决内容来源可靠性和可信度在自动化摘要中的问题。我们的方法在公开的医学信息市场重症监护III（MIMIC-III）语料库以及匿名医院医生撰写的临床笔记上表现出令人印象深刻的可靠性。 提供我们的方法、生成的出院总结输出示例、源代码和训练好的模型。
URL
https://arxiv.org/abs/2506.14101
