利用自然语言处理和高维度临床笔记来检测护理目标和代理人指定的对话
Alaa Albashayreh1, Keela Herr1, Weiguo Fan1
1University of Iowa, IA, USA.
Clinical nursing research
|October 31, 2024
概括
预先的护理规划文件,包括护理目标和代孕指定的,有效地使用自然语言处理 (NLP) 识别. 这项研究突出了影响这些关键对话的晚期记录模式和患者因素.
科学领域:
- 医疗信息学 医疗信息学
- 临床文档分析 临床文档分析
- 在医疗保健中的自然语言处理.
背景情况:
- 预先护理规划 (ACP) 对于以患者为中心的护理至关重要,包括护理目标和代孕指定的.
- 在临床实践中,在ACP讨论中确定最佳时间和参与者仍然具有挑战性.
- 在临床笔记中准确记录ACP元素对于护理的连续性至关重要.
研究的目的:
- 在临床笔记中探索记录护理目标和代孕妇指定对话的频率,时间和预测因素.
- 评估自然语言处理 (NLP) 模型在检测电子健康记录中的ACP对话中的有效性.
- 识别与事先护理规划文件相关的时间模式和患者特征.
主要方法:
- 追溯观察性研究,分析美国中西部一家医院的临床记录.
- 利用高维数据和NLP,包括基于变压器的模型 (BERT),来检测ACP对话.
- 用于模型培训和验证的黄金标准数据集 (n=913笔记) 的手动标签,实现了90.5%的注释者间协议.
主要成果:
- 在检测护理目标和代孕指定的对话中,BERT模型取得了高准确性 (F1得分为93.6).
- 在4341名患者记录中,在85% (护理目标) 和60% (代孕指定的) 中检测到ACP对话.
- 在死亡前平均28天和8天记录了关于护理目标和代孕指定的对话;患者的特征和息护理转诊是显著的预测因素.
结论:
- 在临床叙述中,NLP,特别是BERT,可以准确地检测预先护理规划对话.
- 确定了重要的时间模式,包括迟到的文档,强调了早期干预的必要性.
- 了解与ACP文件相关的患者特征可以为改善以患者为中心的护理和及时讨论的策略提供信息.
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