使护理变得可见:人工智能辅助的电子健康记录干预措施的标准化,使用生成式预训练的变压器模型和检索增强生成过程
Tamara G R Macieira1, Ragnhildur I Bjarnadottir1, Patricia de Oliveira Salgado2
1Department of Family, Community and Health System Science, College of Nursing, University of Florida, Gainesville, FL.
Nursing outlook
|July 25, 2025
概括
生成式预训练变压器 (GPT) 模型在电子健康记录中标准化护理干预术语方面显示出前景,减少了人类工作量. 这些人工智能工具虽然不是完全自动化的,但有助于对护理实践的数据驱动评估.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 护理数据标准 护理数据标准
背景情况:
- 护理对患者的治疗结果有很大影响,但由于缺乏标准化的护士生成数据,护理的研究不足.
- 在电子健康记录 (EHR) 中标准化护理干预术语对于大规模数据分析至关重要.
研究的目的:
- 评估提取增强生成 (RAG) 和生成预训练变压器 (GPT) 模型在标准化护理干预术语中的性能.
- 评估人工智能模型在为护理术语创建"黄金标准"数据集中的实用性.
主要方法:
- 手动将当地护理术语映射到标准化术语,以创建一个黄金标准数据集.
- 实施和评估RAG管道,使用GPT模型进行术语标准化.
- 将AI模型输出集成到人工智能辅助的手动映射工作流中.
主要成果:
- 在标准化护理术语方面,生成预训练变压器 (GPT) 模型表现出与人类映射器相当的性能.
- 对于护理干预术语的完全自动化标准化,GPT模型的准确性是不够的.
结论:
- 人工智能模型,特别是GPT,可以显著减少人类的工作量,并简化护理干预数据的标准化.
- 人工智能辅助标准化的进步对于使护理贡献变得可见至关重要,并使护理实践的数据驱动评估成为可能.
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