可穿戴设备数据和多尺度分析对改善医院再接收预测的影响
Vishal Nagarajan1, Supreeth Prajwal Shashikumar1, Atul Malhotra1
1Department of Medicine, University of California San Diego, La Jolla, CA 92103, United States.
Journal of the American Medical Informatics Association : JAMIA
|September 20, 2024
概括
包括多尺度在内的可穿戴设备数据可以显著改善深度学习模型,用于预测计划外的90天医院再入院. 这种方法提高了患者的风险识别,并可能指导未来的医疗保健策略.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 生物医学数据科学 生物医学数据科学
背景情况:
- 在医疗保健中,非计划性住院再接收是一个重大挑战,尽管正在努力减少其发生.
- 可穿戴设备为主动识别具有再入院风险较高的患者提供了一个有前途的途径.
研究的目的:
- 开发和评估深度学习模型,以使用可穿戴设备数据预测每日,无计划的90天住院再接收.
- 评估来自可穿戴数据的多尺度特征对再接收预测准确性的贡献.
主要方法:
- 一个多中心的回顾性队列研究,利用我们所有人的数据库.
- 推进神经网络 (FNN) 和长期短期记忆 (LSTM) 模型的开发,包括人口统计,实验室和可穿戴数据.
- 转换特征重要性测试以确定关键预测因素,包括可穿戴时间序列的多尺度特征.
主要成果:
- 与FNN模型 (0.795) 相比,LSTM模型表现出优异的性能,接收器操作特征曲线下的面积为0.83.
- 关键的预测特征包括与日常步骤和活跃步骤数量相关的多尺度度.
结论:
- 可穿戴设备数据,特别是多尺度特征,大大提高了深度学习模型的预测能力,用于计划外的90天再录取.
- 调查结果表明,整合可穿戴数据可以完善再接收风险分层,这需要在前性研究中进一步调查.
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