English(EN) Few-Shot Large Language Models for Actionable Triage Categorization of Online Patient Inquiries

大语言模型在患者咨询分诊方面展现潜力，但不能自主部署

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-15 07:02

研究人员探索了使用少样本大语言模型对在线患者咨询进行分类，旨在改进临床分诊。他们使用构建的评估集，将提示式大语言模型与TF-IDF和BioBERT等传统方法进行了比较。虽然最强的大语言模型Claude Haiku 4.5在性能上优于监督基线，但结论是大语言模型可以辅助分诊优先级排序和选择性人工审查，而不是自主部署。 AI

影响大语言模型可以辅助临床审查的患者咨询优先级排序，提高医疗保健环境的效率和安全性。

排序理由该集群包含一篇学术论文，详细介绍了关于大语言模型在特定任务能力的 പഠനം。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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arXiv cs.CL TIER_1 English(EN) · Jiafu Li · 2026-05-15 07:02

用于可操作分诊分类在线患者咨询的少样本大型语言模型

Online patient inquiries are often informal, incomplete, and written before professional assessment, yet they must still be routed to an appropriate level of clinical follow-up. We study this as a four-class actionable triage task -- self-care, schedule-visit, urgent-clinician-re…

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用于可操作分诊分类在线患者咨询的少样本大型语言模型

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