将对话转化为事件数据:从基于GPT的护理行动识别中吸取教训
Iris Beerepoot1, Sjaak Brinkkemper1, Elke Huntink2
1Utrecht University, Heidelberglaan 8, Utrecht, 3584 CS, The Netherlands.
Journal of biomedical informatics
|November 16, 2025
概括
大型语言模型 (LLM) 可以从家庭护理对话中提取护理干预,帮助文档. 为有效使用提出了指导方针,尽管专家监督对于准确性至关重要.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 护理过程分析
背景情况:
- 家庭护理需要大量的文件,这增加了大量的行政负担.
- 结构化的事件日志对于过程分析和医疗保健质量改善至关重要.
研究的目的:
- 评估使用大型语言模型 (LLM) 来从家庭护理护理对话中生成结构化事件日志的可行性.
- 减少医疗保健专业人员的文档负担,并使数据驱动的过程洞察成为可能.
主要方法:
- 一项探索性研究使用了27个转录的家庭护理访问录音.
- 使用生成预训练变压器 (GPT) 模型,使用护理干预分类 (NIC) 系统识别护理干预.
- 代提示工程和评估涉及计算机科学家和护士研究人员.
主要成果:
- GPT表现出在提取明确声明和时间一致的护理干预措施方面的能力.
- 隐含或模两可的信息对准确的提取提出了挑战.
- 为优化LLM在这个领域的应用,制定了五条指导方针,涉及数据,标签,信心和输出定制.
结论:
- 终身医学证明了将非结构化临床对话转化为结构化护理数据的潜力.
- 专家监督和快速改进对于在医疗保健中可靠的LLM应用至关重要.
- 需要仔细实施,以确保准确性,透明度和对现实世界医疗保健环境的信任.
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