通过深度学习和基于图形的建模,早期检测认知衰退.
Sunita Patil1, Swetta Kukreja1
1Computer Science and Engineering, Amity School of Engineering and Technology, Mumbai, Maharashtra 410206, India.
MethodsX
|June 26, 2025
概括
这项研究提出了利用深度学习和多式联络数据进行早期认知障碍检测的新框架. 它提高了认知健康及时干预的准确性和个性化性.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 计算健康 计算健康
背景情况:
- 越来越多的压力和抑郁影响认知健康,需要早期发现认知障碍.
- 传统的认知评估,如蒙特利尔认知评估 (MOCA),在适应性方面存在局限性.
- 动态,数据驱动的方法对于现代认知健康评估至关重要.
研究的目的:
- 引入多模式融合认知评估框架,以提升认知障碍的早期识别.
- 开发一个可扩展,个性化和适应性的认知评估系统.
- 改善早期检测并支持针对认知健康的有针对性的干预策略.
主要方法:
- 综合多式深度学习,包括MOCA分数,行为数据,语音信号和生理参数.
- 使用图形注意力网络 (GAT),变压器注意力 (TAT) 和卷积神经网络-长期短期记忆 (CNN-LSTM) 模型.
- 使用信息融合通过异质图神经网络 (GNN) 进行跨域数据合并.
- 强化学习 (RL) 的应用用于基于实时认知和压力线索的个性化用户交互.
主要成果:
- 拟议的模型通过有效的跨模式学习,在认知状态评估中实现了卓越的性能.
- 不同质的GNN成功地将各种数据源合并为整体的认知评估.
- 强化学习可以个性化交互,减少认知过载,提高用户参与度.
结论:
- 多模式融合认知评估框架显著提高了认知障碍早期检测的准确性.
- 这种数据驱动的方法为传统的认知测试提供了更具活力和个性化的替代方案.
- 该框架支持及时干预和改善现代世界认知健康的管理.
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