向警报分类:可扩展的定性编码框架,用于分析来自老年人远程医疗干预计划 (TIPS) 的警报笔记
Phuong Nguyen1, Melody K Schiaffino2, Zhan Zhang3
1Department of Computer Science, University of Iowa, Iowa City, Iowa, USA.
JAMIA open
|August 10, 2023
概括
这项研究开发了一个自动化系统,从护士的笔记中分类远程医疗警报,改进数据分类. 该方法显示高精度和回忆,支持远程医疗的采用和减少临床医生的倦怠.
科学领域:
- 数字健康数字健康
- 医疗信息学 医疗信息学
- 临床数据分析 临床数据分析
背景情况:
- 远程医疗产生了大量的数据,冒着临床医生倦怠和错过关键患者信息的风险.
- 有效的数据分类对于成功实施远程医疗计划和患者安全至关重要.
研究的目的:
- 开发护士笔记的自动分类框架,为警报分类系统创建培训数据集.
- 提高远程医疗计划数据管理的效率和准确性.
主要方法:
- 一个编码框架和基于正则表达式的关键字匹配是使用24931个警报笔记来开发的.
- 自动化警报分类模型的可扩展性和性能在800个采样警报笔记上进行了评估.
主要成果:
- 92%的警报通知 (22,717/24,579) 已成功分为17个代码.
- 自动化分析实现了0.86的平均精度和0.90的回忆.
结论:
- 开发的方法是可行的和可扩展的基于社区的远程医疗,特别是老年人.
- 编码的警报笔记可以改善预测建模和虚假警报分类,推进远程健康监测系统.
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