从分诊到出院：急诊科NLP任务、方法与挑战综述
原标题：From Triage to Discharge: A Survey of NLP Tasks, Methods, and Open Challenges in the Emergency Department
AI 摘要
这篇综述分析了46篇关于急诊科（ED）自然语言处理（NLP）的论文，覆盖分诊、诊断和处置三个阶段，涉及分诊分类、临床摘要、自动诊断、报告生成和出院文档等任务。研究发现，研究趋势从任务特定架构转向预训练语言模型，并越来越关注交互式临床系统和临床评估。文章还指出了泛化性有限、临床输入噪声和工作流约束等开放挑战。
正文节选
From Triage to Discharge: A Survey of NLP Tasks, Methods, and Open Challenges in the Emergency Department Abstract Emergency departments (EDs) operate under time pressure, generating multimodal data such as clinical conversations, triage notes, and discharge documents. Recent advances in natural language processing (NLP), particularly pretrained transformers and large language models, have created new opportunities to support language and time-intensive stages of emergency care. Yet existing sur