Problem
Research questions and friction points this paper is trying to address.
Discharge Summaries
Hallucinations
Clinical Documentation
Toward Complete Hospital Discharge Summarization with Abstract Meaning Representation
🤖 AI Summary
本文针对医院出院摘要自动生成问题，提出一种基于语义图和深度学习模型的证据驱动对齐框架，以减少临床文档错误。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
evidence-driven alignment
semantic graphs
deep learning models
cross-document semantic alignment
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