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Updated: Jun 28, 2025
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Conducting Respiratory Oscillometry in an Outpatient Setting
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评估基于国际护理实践分类 (International Classifications of Nursing Practice for respiratory nursing care) 基于使用大型语言模型算法生成的护理陈述的有效性
Hyeoneui Kim1,2,3, Hyewon Park1,4, Sunghoon Kang5
1College of Nursing, Seoul National University, Seoul, 03080, Republic of Korea.
概括
生物临床BERT有效验证算法生成的护理陈述,改善医疗保健文档. 这种人工智能方法增强了使用国际护理实践分类来创建标准化的护理陈述.
科学领域:
- 护理信息学 护理信息学
- 医疗保健中的人工智能
- 临床文档 临床文档
背景情况:
- 标准化护理陈述对于质量护理和数据的一致性至关重要.
- 目前创建护理陈述的方法可能耗时且缺乏标准化.
- 算法生成为高效和一致的语句创建提供了一个潜在的解决方案.
研究的目的:
- 开发和评估一种算法方法,用于在韩国生成标准化的护理陈述.
- 评估大型语言模型 (LLM) 在验证这些生成语句方面的有效性.
- 为了利用国际护理实践分类 (ICNP) 来生成陈述.
主要方法:
- 通过使用ICNP概念,算法生成了15972份急性呼吸护理护理陈述.
- 使用人类审查员,GPT-4.0和BioClinicalBERT评估声明有效性.
- 评估LLM表现,有或没有上下文信息和培训.
主要成果:
- 2,207个陈述被人类专家验证.
- GPT-4.0的AUC为0.857 (零射击),随着环境变化而下降.
- 在培训后,BioClinicalBERT 实现了 0.998 的 AUC,显示出高精度.
结论:
- 在验证算法生成的护理陈述方面,BioClinicalBERT表现出卓越的性能.
- 人工智能支持的验证提供了一种可扩展和有效的方法来增强医疗保健文档.
- 这种方法有助于创建高质量的,标准化的护理声明.
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