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通过临床语言模型增强不良事件报告:住院患者跌倒
Insook Cho1,2, Hyunchul Park3,4,5, Byeong Sun Park6
1College of Nursing, Inha University, Incheon, Republic of Korea.
Journal of advanced nursing
|February 13, 2025
概括
临床语言模型可以通过计算检测患者跌倒,改善不良事件跟踪和减少护士负担. 这种方法可以识别未报告的跌倒,补充现有的自我报告机制,以提高准确性.
科学领域:
- 自然语言处理自然语言处理.
- 临床信息学 临床信息学
- 患者安全 患者安全
背景情况:
- 住院患者的摔倒往往被低估,自报告机制在91%的事件中缺失.
- 准确追踪像跌倒这样的不良事件对于患者安全和质量改善至关重要.
- 现有的落检测方法通常依赖于手动报告,这可能是繁和不完整的.
研究的目的:
- 用临床语言模型开发和评估一种用于检测患者跌倒事件的计算方法.
- 评估各种语言模型的性能,包括来自变压器的双向编码器表示 (BERT) 和生成预训练变压器 (GPT) -4,用于识别临床笔记中的失误.
- 为了比较提示程序的有效性与GPT-4在摔倒检测中的标准化提示.
主要方法:
- 一项回顾性观察性研究,利用来自电子健康记录和国家患者安全报告的非结构化护理笔记.
- 数据预处理涉及匿名化,英语翻译和34,480条记录 (2015年1月 - 2019年12月) 的语义验证.
- 研究了五种语言模型,包括精心调整的BERT和具有快速编程的GPT-4,通过F1分数和错误分析来衡量性能.
主要成果:
- 微调的BERT模型实现了最高的性能,Bio+Clinical BERT和韩国BERT都达到0.98.98的F1得分.
- 与标准化提示相比,具有提示编程的GPT-4显著提高了性能 (韩国数据的F1分数为0.94,英语数据为0.85).
- 常见的错误包括错误分类由于跌倒历史,同名词 (假阳性),隐含的表达式或缺失的上下文 (假阴性).
结论:
- 临床语言模型提供了一个有前途的方法来计算检测患者跌倒,大大提高了未报告事件的识别.
- 这种方法可以提高不良事件跟踪的准确性,并减少对护士手动自我报告的依赖.
- 将基于语言模型的跌倒检测与现有的自我报告系统相结合,可以带来更全面,更准确的患者安全监测.
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