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临床表现 临床表现

Bargav Jagatha1,2, Ting Fang Alvin Ang3,4,5, Rhoda Au3,4,5,6,7,8

  • 1Department of Medicine/Section of Preventive Medicine and Epidemiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.

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概括
此摘要是机器生成的。

深度学习模型在阿尔茨海默病 (AD) 研究中有效地归咎于缺失的认知数据. 该SAITS模型在处理纵向数据差距方面表现出卓越的性能,提高了临床研究的准确性.

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科学领域:

  • 神经学 神经学
  • 数据科学数据科学数据科学
  • 生物统计学 生物统计学

背景情况:

  • 阿尔茨海默病 (AD) 研究中的纵向认知评估通常由于后勤,个人或与健康有关的问题而缺乏数据.
  • 缺少的数据对临床研究和患者监测构成挑战,需要强大的归算方法.
  • 深度学习方法为纵向数据的传统归算技术提供了有希望的,无假设的替代方案.

研究的目的:

  • 评估基于深度学习的归算方法在阿尔茨海默病 (AD) 中缺少纵向认知数据的有效性.
  • 评估基于自我注意的时间序列推算 (SAITS) 模型在AD研究中的认知分数推算中的性能.

主要方法:

  • 开发并应用SAITS深度学习模型,以计算迷你精神状态检查 (MMSE) 和阿尔茨海默病评估尺度-认知 (ADAS-cog) 评分中的缺失值.
  • 使用来自GERAS-EU和GERAS-US研究的纵向数据进行培训和评估的SAITS,将其与iTransformer,BRITS,平均归算和最后的观察转移 (LOCF) 进行比较.
  • 在100个试验的随机掩盖测试数据上,使用平均绝对误差 (MAE) 评估了性能.

主要成果:

  • 在GERAS-EU和GERAS-US群体中,SAITS在归因认知数据方面表现出卓越的表现.
  • 实现了最低的MAE值,用于归纳ADAS-cog总分 (1.636在GERAS-EU, 1.628在GERAS-US) 和MMSE总分 (0.567在GERAS-EU, 1.178在GERAS-US).

结论:

  • 深度学习技术,特别是SAITS,显示出在AD中准确地归因错过的纵向认知数据的巨大潜力.
  • 未来的研究将探索这些归算方法对下游任务的影响,例如预测AD患者的认知衰退.