系统梳理急诊科自然语言处理任务与挑战
From Triage to Discharge: A Survey of NLP Tasks, Methods, and Open Challenges in the Emergency Department
- 分析急诊三阶段共46篇论文，覆盖分诊、诊断到出院全流程
- 发现从专用模型转向预训练大模型的主流趋势
- 适合关注临床NLP落地的医疗AI研究者参考
急诊科运行时间紧迫，产生包括临床对话、分诊记录和出院文档在内的多模态数据。近年来，自然语言处理技术，特别是预训练变压器和大语言模型的发展，为支持急诊中语言密集且耗时的环节带来了新机遇。然而现有综述多覆盖整个医院流程或聚焦单一任务。本综述分析了涵盖急诊三个阶段（分诊、诊断、处置）的46篇论文，涉及分诊分类、临床摘要生成、自动诊断、报告生成和出院文档等任务。研究考察了建模范式、评估实践及新兴基准与共享任务。跨任务分析显示共同趋势：从任务专用神经架构转向预训练语言模型，对交互式临床系统兴趣上升，临床可解释性评估日益重要。最后，指出泛化能力有限、临床输入噪声多、工作流约束等开放挑战，为未来急诊科NLP研究提供方向。
原文摘要 · Abstract (English)
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.
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