用语言图结构提升病历摘要生成可信度，减少大模型幻觉
Abstract Meaning Representation for Hospital Discharge Summarization
- 结合语言图与深度学习，追踪摘要内容来源
- 在MIMIC-III和匿名医院数据上表现可靠
- 适合医疗AI、临床自然语言处理研究者
大型语言模型（LLMs）的致命弱点是幻觉，这对临床领域影响巨大。自动生成出院摘要（总结住院过程的长篇医疗文档）可减轻医生负担，提升效率。本文探索融合基于语言的图结构与深度学习模型的新方法，以增强自动摘要的内容可追溯性与可信度。实验在公开的MIMIC-III语料库及匿名医院医生书写的临床笔记上取得优异可靠性结果。本文提供方法、生成样例、源代码及训练模型。
原文摘要 · Abstract (English)
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.
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