发表机构
University of New South Wales（新南威尔士大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本综述分析46篇论文，梳理急诊部门分诊、诊断、处置阶段的NLP任务与方法，指出模型从特定架构向预训练语言模型转变等趋势，明确泛化能力有限等开放挑战，为相关研究提供方向。
AI 中文摘要
急诊部门（ED）在时间压力下运作，会产生临床对话、分诊记录、出院文档等多模态数据。自然语言处理（NLP）的近期进展，尤其是预训练Transformer和大语言模型，为支持急诊护理中耗时的语言相关阶段创造了新机遇。然而现有综述要么涵盖更广泛医院流程中的临床NLP，要么聚焦特定任务。本综述分析了46篇论文，涵盖ED的三个阶段：分诊、诊断与处置，涉及分诊分类、临床摘要、自动诊断、报告生成及出院文档等任务。我们考察了建模范式、评估实践、新兴基准与共享任务，在各任务中识别出共性趋势，包括从特定任务神经架构向预训练语言模型的转变、对交互式临床系统的兴趣增长，以及对基于临床的评估的关注提升。最后，我们详述了开放挑战，如泛化能力有限、临床输入噪声大、工作流程约束等，这些为未来ED-NLP研究提供了方向。
英文摘要
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 surveys map clinical NLP across the broader hospital workflow or focus on specific tasks. This survey analyses 46 papers spanning the three phases of ED: triage, diagnosis, and disposition, covering tasks such as triage classification, clinical summarisation, automatic diagnosis, report generation, and discharge documentation. We examine modelling paradigms, evaluation practices, and emerging benchmarks and shared tasks. Across tasks, we identify common trends, including a shift from task-specific neural architectures to pretrained language models, growing interest in interactive clinical systems, and increasing attention to clinically grounded evaluation. Finally, we detail open challenges such as limited generalisability, noisy clinical inputs, and workflow constraints that inform future ED-NLP research.
CommentsAccepted to the EMNLP 2026 Main Conference; camera-ready version
