预测神经精神症状和阿尔茨海默病生物标志物的认知衰退:一种基于人口数据的机器学习方法
Jay Shah1,2, Janina Krell-Roesch3,4, Erica Forzani5
1School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA.
Journal of Alzheimer's disease : JAD
|January 10, 2025
概括
在机器学习模型中包括神经精神症状 (NPS) 改善了与阿尔茨海默病 (AD) 生物标志物相结合时,对老年人认知衰退的预测. 这种增强有助于识别面临认知变化风险的个体.
科学领域:
- 老年学是一门学科.
- 神经科学是一个神经科学.
- 人工智能的人工智能
背景情况:
- 预测老年人认知能力下降对于及时干预至关重要.
- 阿尔茨海默病 (AD) 生物标志物和人口统计数据是已知的预测因素.
- 在预测模型中神经精神症状 (NPS) 的附加值仍未得到充分研究.
研究的目的:
- 评估NPS在机器学习 (ML) 模型中对认知衰退的增量预测值.
- 为了比较ML模型的性能与没有NPS,以及AD生物标志物和人口统计数据.
- 确定特定的认知领域,其中NPS包含最影响预测准确性.
主要方法:
- 利用了梅奥诊所老龄化研究 (参与者≥50岁) 的数据.
- 包括人口统计,NPS (神经精神病学库存,贝克抑郁症/焦虑库存) 和AD生物标志物 (血,神经成像,脑脊液).
- 经过训练和测试的ML模型使用逐步的功能添加来预测认知衰退 (全球和特定领域).
主要成果:
- 当NPS与AD生物标志物集成时,ML模型显示性能有所改善.
- 观察到全球认知,语言,视觉空间技能 (用神经成像) 的增强预测.
- NPS改善了视觉空间技能 (用血生物标志物) 和注意力/执行功能,语言,记忆 (用CSF生物标志物) 的预测.
结论:
- 当添加到具有人口统计和AD生物标志物数据的ML模型中时,NPS可以增强对老年人认知能力下降的预测.
- 虽然效果大小很小,但NPS为各种认知领域提供了有价值的预测信息.
- 建议在前性队列研究中进一步验证以确认这些初步发现.
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