深度表示学习识别了怀孕和早产期间身体活动和睡眠模式之间的关联
Neal G Ravindra1,2,3, Camilo Espinosa1,2,3, Eloïse Berson1,3,4
1Department of Anesthesiology, Perioperative and Pain Medicine, Stanford School of Medicine, Stanford, CA, USA.
NPJ digital medicine
|September 28, 2023
概括
一个新的深度学习模型使用可穿戴数据来跟踪健康怀孕的进展. 身体活动和睡眠模式的偏差标志着早产风险的改变,使得有针对性的干预措施成为可能.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
- 孕产妇和儿童的健康
背景情况:
- 过早分娩 (PTB) 是全球领先的婴儿死亡原因.
- 现有的PTB预测模型往往忽视了适合低收入和中等收入人群 (LMICs) 的成本有效干预措施.
- 对身体活动和睡眠的客观测量至关重要,但具有挑战性;自我报告的数据缺乏准确性.
研究的目的:
- 开发一种深度学习模型,使用可穿戴体育活动数据跟踪健康妊娠动态.
- 创建可解释性算法,以理解与睡眠,活动和临床变量相关的模型预测.
- 评估偏离预测妊娠进展和早产风险之间的关联.
主要方法:
- 通过可穿戴设备利用了来自1083名患者的超过181,944小时的体育活动数据.
- 开发了一种深度学习时间序列分类架构,以使用妊娠年龄 (GA) 作为代孕来建模妊娠进展.
- 实施了新的可解释性算法,包括集群,错误分析和特征归属.
主要成果:
- 深度学习模型在模拟怀孕进展方面表现优于其他七种机器学习方法.
- 与预测的身体活动和睡眠"时钟"的偏差与PTB风险有显著的相关性.
- 模型低估GA与较少的PTB相关,而高估则表明PTB风险较高 (分别P < 1.01e-67和P < 2.82e-39).
- 模型错误与日间稳定性有负相关性,睡眠模式是GA估计的重要预测因素.
结论:
- 模型检测到的怀孕期间身体活动和睡眠动态的偏差是早产风险的强有力的指标.
- 该模型的可解释性允许识别什么时候行为变化会影响PTB的可能性.
- 倡导使用被动监测和个性化运动和睡眠建议的临床决策支持系统,特别是在LMICs.
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