机器学习方法改善了从轻度认知障碍到阿尔茨海默病的进展的纵向预测
Robert P Adelson1, Anurag Garikipati1, Jenish Maharjan1
1Montera, Inc. dba Forta, 548 Market St, PMB 89605, San Francisco, CA 94104-5401, USA.
Diagnostics (Basel, Switzerland)
|January 11, 2024
概括
一个新的机器学习算法 (MLA) 准确地预测了轻度认知障碍 (MCI) 患者的阿尔茨海默病风险. 这种工具在改善早期检测和患者护理方面表现有前途.
科学领域:
- 神经学 神经学
- 人工智能的人工智能
- 生物医学数据科学 生物医学数据科学
背景情况:
- 轻度认知障碍 (MCI) 是阿尔茨海默病 (AD) 的前体,需要准确的预测工具.
- 目前用于预测MCI的AD进展的方法有局限性.
- 早期识别患有阿尔茨海默病高风险的个体对于及时干预至关重要.
研究的目的:
- 开发和验证一种机器学习算法 (MLA),用于预测MCI向AD的进展.
- 将MLA的预测性能与标准临床评估和其他模型进行比较.
主要方法:
- 开发了一个渐变增强的树组合机器学习算法.
- 分析了493名被诊断患有MCI的个体 (55-88岁) 的表型数据.
- 在多个时间窗口 (24-48个月) 进行了预测,最终评估使用了平均数据.
主要成果:
- 与迷你心理状态考试 (MMSE) 和大多数指标和预测窗口中的其他模型相比,MLA表现优越.
- 该MLA始终实现了接收器运行特征曲线 (AUROC) 下的面积≥0.857和负预测值 (NPV) ≥0.800.
- 虽然MMSE在特定的后期时间点 (30,42个月) 显示出稍微更好的灵敏度,但在使用24-48个月时间框架的平均数据时,MLA在所有指标上都表现出色.
结论:
- 机器学习算法,如开发的MLA,比目前的标准护理提供了更准确的AD从MCI进展的风险评估.
- 改善风险分层可以促进更好的护理协调,潜在地降低医疗保健成本,并提高风险人群的生活质量.
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