面向完整出院总结的抽象意义表示方法
Toward Complete Hospital Discharge Summarization with Abstract Meaning Representation
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中文总结 AI 辅助
针对大型语言模型生成出院总结时的幻觉问题，提出基于证据的对齐框架，利用语义图和深度学习实现来源可溯的总结生成，并在MIMIC-III和UIC Health语料库上验证效果。
中文摘要 AI 辅助
出院总结是概括住院患者就诊过程的冗长医疗文档。自动生成出院总结可以减轻文档负担，并将临床医生的时间归还给患者护理。虽然大型语言模型（LLMs）可用于此任务，但其致命弱点是幻觉，这可能对临床文档产生严重后果。我们提出了一种在临床就诊层面进行出院总结的基于证据的对齐框架，该框架将来源可溯性作为首要约束，利用语义图和深度学习模型。每个总结句子通过跨文档语义对齐进行选择和组织，并附有指向其来源片段的明确证据链接。我们在两个语料库上展示了结果：一个公开可用的语料库（MIMIC-III）和伊利诺伊大学医院（UIC Health）医生撰写的临床笔记。此外，我们提供了源代码和训练好的模型。
英文摘要
Discharge summaries are lengthy medical documents that summarize a hospital in-patient visit. Automatically generating them can reduce documentation burden and return clinician time to patient care. Whereas Large Language Model (LLMs) could be used for this task, their Achilles heel is hallucinations, which can have drastic consequences for clinical documentation. We present an evidence-driven alignment framework for discharge summarization at the clinical encounter level, that treats provenance as a first-class constraint, using semantic graphs and deep learning models. Each summary sentence is selected and organized via cross-document semantic alignment and is accompanied by explicit evidence links to its source spans. We show our results on two corpora: a publicly available corpus (MIMIC-III) and clinical notes written by physicians at the University of Illinois Hospital (UIC Health). Additionally, we make source code and trained models available.
发表机构
- University of Illinois Chicago（伊利诺伊大学芝加哥分校）
机构由 AI 辅助整理，请以论文原文为准。
