穿戴式传感器用于重症监护中的患者敏度评估
Jessica Sena1, Mohammad Tahsin Mostafiz2, Jiaqing Zhang2
1Department of Computer Science, Federal University of Minas Gerais, Belo Horizonte, Brazil.
可穿戴式传感器和电子健康记录提高了重症监护中的患者敏度评估. 将移动数据与临床信息相结合,比传统方法提高了预测准确度.
科学领域:
- 关键护理医学 关键护理医学
- 生物医学工程 生物医学工程
- 医疗信息学 医疗信息学
背景情况:
- 在重症监护机构中,传统的敏度评分系统是主观的,容易产生偏见和错误,并且往往无法捕捉到细粒度的患者状态.
- 现有的方法不包括动态患者信息,如移动性,这对于在重症监护室 (ICU) 显示康复或恶化至关重要.
研究的目的:
- 评估将可穿戴加速度计的移动数据与电子健康记录 (EHR) 临床数据相结合的影响,以改善患者敏度评估.
- 为了确定使用综合传感器和EHR数据的深度学习模型是否可以在预测患者敏度状态方面超过传统的评分系统.
主要方法:
- 一项前性研究涉及87名在学术医院环境中的患者,从手腕佩戴传感器收集加速计数据.
- 使用五种深度神经网络模型 (VGG,ResNet,MobileNet,SqueezeNet,Transformer) 来分析加速度计数据与EHR信息相结合.
- 将这些模型的性能与顺序器官衰竭评估 (SOFA) 评分进行比较,这是一个标准的临床敏度评分.
主要成果:
- 集加速度计,人口统计和临床数据的深度学习模型显著超过了SOFA得分基线.
- 综合模型实现了0.73的接收机运行特征 (ROC) 曲线 (AUC) 下的更高面积,而SOFA的0.53.
- 在使用综合数据方法时,观察到精度 (0.80对0.23),特异性 (0.79对0.73) 和F1得分 (0.77对0.66) 的显著改善.
结论:
- 将可穿戴传感器衍生的移动数据与EHR信息集成,为评估重症监护中的患者敏度提供了更加细致和准确的方法.
- 与传统的评分系统相比,深度学习方法在敏度预测方面表现优越,突出了技术在增强患者监测方面的潜力.
- 这种新的方法提供了对患者状况的更全面的理解,超越了简单的稳定/不稳定分类,为临床决策提供了有价值的见解.
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