通过语言模型赋能医疗从业者：在两个真实临床应用中结构化语音转录
Empowering Healthcare Practitioners with Language Models: Structuring Speech Transcripts in Two Real-World Clinical Applications
AI总结：
本文通过语言模型在两个临床应用中结构化语音转录，提出代理流程生成非敏感数据，并发布首个开源数据集以支持进一步研究。
摘要：
大型语言模型（如GPT-4o和o1）在多个医学基准测试中展示了在临床自然语言处理（NLP）任务中的强大性能。然而，两个高影响力NLP任务——从护士口述转录中生成结构化表格报告和从医生-患者咨询中提取医疗订单——由于数据稀缺性和敏感性仍处于未探索状态，尽管行业正在积极努力。解决这些现实临床任务的实用方案可以显著减少医疗从业者的工作负担，使他们能更专注于患者护理。在本文中，我们利用私有和开源的临床数据集研究了这两个具有挑战性的任务，评估了开放式和封闭式权重LLM的性能，并分析了各自的优势和局限性。此外，我们提出了一种代理流程，用于生成真实且非敏感的护士口述转录，以实现结构化临床观察的提取。为了支持这两个领域的进一步研究，我们发布了SYNUR和SIMORD，这是第一个开源的数据集，用于护士观察提取和医疗订单提取。
英文摘要：
Large language models (LLMs) such as GPT-4o and o1 have demonstrated strong performance on clinical natural language processing (NLP) tasks across multiple medical benchmarks. Nonetheless, two high-impact NLP tasks - structured tabular reporting from nurse dictations and medical order extraction from doctor-patient consultations - remain underexplored due to data scarcity and sensitivity, despite active industry efforts. Practical solutions to these real-world clinical tasks can significantly reduce the documentation burden on healthcare providers, allowing greater focus on patient care. In this paper, we investigate these two challenging tasks using private and open-source clinical datasets, evaluating the performance of both open- and closed-weight LLMs, and analyzing their respective strengths and limitations. Furthermore, we propose an agentic pipeline for generating realistic, non-sensitive nurse dictations, enabling structured extraction of clinical observations. To support further research in both areas, we release SYNUR and SIMORD, the first open-source datasets for nurse observation extraction and medical order extraction.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
