基于可穿戴设备和患者报告的结果测量,用于估计晚期癌症患者的生活质量的一种多模式深度学习架构
IEEE journal of biomedical and health informatics
|August 11, 2025
概括
这项研究使用人工智能和可穿戴设备持续监测晚期癌症患者.
科学领域:
- 在瘤学瘤学.
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 传统的瘤学依赖于临床指标来监测晚期癌症患者.
- 患者报告结果测量 (PROMs) 提供了对健康相关生活质量 (HRQoL) 的见解,但也带来了后勤挑战.
- 可穿戴设备和人工智能提供连续的,实时的HRQoL监控功能.
研究的目的:
- 引入多模式深度学习方法来估计晚期癌症患者的HRQoL.
- 将可穿戴设备的生理数据与PROM集成在一起,以便全面监测患者.
主要方法:
- 一种混合深度学习模型,将CNN和BiLSTM与注意力结合起来,用于生理数据.
- 通过并行神经网络处理PROM (HADS,IPOS).
- 生理学和PROM数据被整合用于HRQoL预测.
主要成果:
- 多式联络模型在HRQoL预测中实现了0.24的平均绝对百分比误差 (MAPE).
- 该模型有效地捕获了患者数据中的时间动态和局部模式.
- 验证包括42天内从204名患者获得的数据.
结论:
- 将可穿戴数据和PROM与多式联络深度学习相结合,具有改善先进癌症治疗的巨大潜力.
- 持续的实时监测可以提供对患者健康状况的更全面的了解.
- 这种方法解决了患者异质性和监测的复杂性.
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