揭示跌倒风险因素:护士驱动的自然语言处理库开发
Ragnhildur I Bjarnadottir1, Yonghui Wu1, Urszula A Snigurska1
1University of Florida, Gainesville, FL, USA.
Studies in health technology and informatics
|July 25, 2024
概括
这项研究使用专家护士来注释临床笔记,训练自然语言处理 (NLP) 管道以识别医院跌倒风险. 该方法显示了提高患者安全和提取更广泛的临床见解的潜力.
科学领域:
- 临床信息学 临床信息学
- 护理研究 护理研究
- 自然语言处理自然语言处理.
背景情况:
- 在医院获得的跌倒仍然是一个重要的临床挑战.
- 护士生成的临床笔记包含了预防跌倒的宝贵数据.
- 先进的分析方法,如NLP,为数据利用提供了新的途径.
研究的目的:
- 开发和测试一种自然语言处理 (NLP) 管道,从注册护士 (RN) 的临床笔记中提取与跌倒相关的因素.
- 在数据注释中利用专家护理知识,以改善NLP模型培训.
- 探索注释性临床笔记在预防跌倒之外的潜在用处.
主要方法:
- 采用了RN临床笔记专家手动注释的代过程.
- 这些注释数据被用来训练和测试NLP管道.
- 评估了Interrater可靠性,以确保数据质量.
主要成果:
- 标注的数据库实现了中度高的评分器间可靠性 (F-score=0.74).
- 该NLP管道展示了在提取落相关因素的能力.
- 提取的临床概念显示了超出患者跌倒的潜在实用性.
结论:
- 专家护士注释是培训NLP模型解决临床问题,如跌倒的宝贵资源.
- 开发的NLP管道显示了增强患者安全和提取更广泛的临床见解的前景.
- 需要进一步的研究,以优化在注释任务中使用护理专业知识的效率.
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