预测未来与衰老相关的缓慢步伐及其决定因素,使用深度学习和后勤回归
Alison Deatsch1, Michael McKenna2, Jonathan Palumbo2
1Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.
PloS one
|June 17, 2025
概括
使用深度学习神经网络 (NN) 可以预测缓慢步行,这是加速衰老的指标. 这种方法对识别年龄和BMI等决定因素充满希望,有助于及时干预.
科学领域:
- 老年学和生物医学信息学
- 计算生物学和生物信息学
背景情况:
- 加速衰老的识别对于及时的治疗干预至关重要.
- 慢步是加速衰老的公认预测因素.
- 预测慢步和其决定因素可以为临床决策提供信息.
研究的目的:
- 用深度学习来预测与衰老相关的缓慢步伐.
- 确定导致缓慢步行的关键决定因素.
- 为了比较神经网络 (NN) 与物流回归 (LR) 的性能.
主要方法:
- 深度学习神经网络 (NN) 模型的应用.
- 与传统的物流回归 (LR) 分析进行比较.
- 利用了1363名巴尔的摩长度老龄化研究参与者的数据.
- 在6年和10年的随访期预测当前和未来的慢步.
- 集成的类平衡技术用于模型优化.
- 采用索博尔指数分析来确定步态决定因素.
主要成果:
- 在10年的慢步预测 (0.8m/s切点) 中,NN实现了81.2%的最大灵敏度和87.9%的最大特异性.
- 该NN的表现与LR相比,在某些情况下比LR更好 (最大灵敏度84.5%,特异性86.3%).
- 发现的缓慢步行的关键决定因素包括年龄,BMI,睡眠和握力.
结论:
- 深度学习神经网络为预测慢步提供了一种新且有效的方法.
- 对于传统的物流回归来说,NN模型的性能基准是有利的.
- 这种预测模型的进一步研究和扩展是必要的.
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