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从使用深度学习技术的多式联络数据自动评估患者功能 - - 开发和可行性评估.

Emese Sükei1, Santiago de Leon-Martinez1,2,3, Pablo M Olmos1,4

  • 1Department of Signal Theory and Communications, Universidad Carlos III de Madrid, Av. de la Universidad 30, Leganés 28911, Madrid, Spain.

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概括
此摘要是机器生成的。

这项研究介绍了一个使用可穿戴传感器的AI管道,用于从数字生物标志物预测移动障碍和焦虑. 该模型有效处理噪音数据,表现优于基线,并为远程患者监测提供可解释的见解.

关键词:
注意力模型的注意力模型数字化表型化是指数字化表型化.生态瞬间评价 生态瞬间评价在现场对患者进行监测.时间序列建模时间序列建模转移学习转移学习

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科学领域:

  • 数字健康数字健康
  • 医疗保健中的人工智能
  • 生物医学信号处理

背景情况:

  • 可穿戴设备和移动传感器生成大量实时生理和行为数据.
  • 这些数据为远程患者监测提供了潜力,超越了传统方法.
  • 挑战包括数据噪声和缺失的观测,阻碍了稳健的模型开发.

研究的目的:

  • 开发一条人工智能管道,用于使用可穿戴传感器的数字生物标志物预测移动障碍.
  • 通过转移学习解决数据挑战,如缺失的观测,并利用未标记的数据.
  • 评估模型在现实世界数据集中的性能和可解释性.

主要方法:

  • 提出了一个基于注意力的长短期记忆 (LSTM) 神经网络管道.
  • 隐藏的马尔科夫模型被用来处理丢失的数据.
  • 使用转移学习,将未标记样本的信息纳入.
  • 该方法在两个现实世界可穿戴/移动传感器数据集上得到了验证.

主要成果:

  • 拟议的管道在预测移动障碍 (WHODAS 2.0) 方面表现优于之前的基线.
  • 注意热图为模型的预测提供了可解释性.
  • 使用较小的队列,任务转移学习能够准确预测通用焦虑严重程度 (GAD-7).

结论:

  • 开发的AI管道有效地预测了可穿戴传感器数据的健康结果,如运动障碍和焦虑.
  • 该模型在处理杂和不完整的数据方面表现出稳健性,这对于远程患者监控至关重要.
  • 该方法提供了可解释的见解,并显示了在数字健康领域广泛应用的潜力.