使用电信对话和护理文档来预测基于网络的远程医疗计划中的急诊室访问风险
Hui-Wen Wu1,2, Chi-Sheng Hung1,3, Ying-Hsien Chen1,3
1Telehealth Center, National Taiwan University Hospital, No. 7, Chung-Shan South Road, Taipei 100, Taiwan.
European heart journal. Digital health
|September 23, 2025
概括
自然语言处理可以使用远程医疗对话记录和护理笔记预测慢性病患者的急性恶化. 结合这两种数据源,显著提高了急诊室访问的预测准确度.
科学领域:
- 数字健康数字健康
- 医疗保健中的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 远程医疗保健计划是有效的管理慢性疾病.
- 每天的电信提供了宝贵的患者见解.
- 预测急性恶化对于主动干预至关重要.
研究的目的:
- 调查自然语言处理 (NLP) 在远程医疗计划中用于预测慢性病患者急性恶化.
- 评估护理记录和语音对话对急诊室 (ER) 访问的预测能力.
主要方法:
- 一项回顾性研究,使用来自远程医疗会话和护理笔记的音频成绩单.
- 开发预训练过的基于变压器的神经网络模型,包括BERT-base.
- 模型被训练来预测ER访问在两个星期的时间框架内.
主要成果:
- 一个带有成绩单的BERT基模型在预测ER访问时达到中度准确度 (0.75).
- 将护理记录和成绩单作为输入数据相结合,显著提高了预测性能.
- 组合模型实现了0.892.9的优越整体精度.
结论:
- 预测ER访问是可行的,使用远程医疗对话记录和护理笔记与变压器模型.
- 纳入护理笔记大大提高了模型的性能.
- 这种方法为提高远程医疗保健预测准确度提供了有价值的方法.
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