根据ATN分类来识别临床前痴呆症,用于分层试验招募:一种机器学习方法
Ivan Koychev1, Evgeniy Marinov2, Simon Young1
1Department of Psychiatry, University of Oxford, Oxford, United Kingdom.
PloS one
|October 19, 2023
概括
机器学习准确地预测了临床前阿尔茨海默氏症 (AD) 使用粉样蛋白/陶/神经退行 (ATN) 框架. 与传统方法相比,这种方法可以改善没有痴呆症的个体的风险预测.
科学领域:
- 神经科学是一个神经科学.
- 生物标志物 生物标志物
- 计算生物学 计算生物学
背景情况:
- 粉样蛋白/Tau/神经退行 (ATN) 框架对于识别临床前阿尔茨海默病 (AD) 是至关重要的.
- 使用现有研究数据预测ATN表型对于早期AD检测至关重要.
- 在研究队伍中例行收集数据为ATN表型预测提供了潜力.
研究的目的:
- 用例行收集的研究队列数据调查ATN表型的可预测性.
- 将机器学习 (ML) 预测模型与ATN表型的物流回归进行比较.
- 为准确的ATN表型预测确定一组简洁的特征.
主要方法:
- 利用了927名没有痴呆症或轻度认知障碍的EPAD LCS队列参与者的数据.
- 采用机器学习方法,包括随机森林和线性内核SVM,具有5倍交叉验证.
- 在ADNI数据库上验证了最佳模型,并确定了10个关键预测特征的子集.
主要成果:
- 机器学习模型在ATN交叉验证后勤回归中表现出优异的性能,改进幅度从2.2%到8.3%不等.
- 在各种模型中,最佳特征集各不相同,突出显示了ATN表型预测的复杂性.
- 确定了10个特征的子集,产生了与最佳模型可比的结果.
结论:
- 与后勤回归相比,机器学习显著提高了在痴呆前患者的ATN风险预测.
- 该研究验证了ML对预测临床前AD状态的有用性,使用现有的队列数据.
- 识别的关键特征为早期AD研究中ATN表型预测提供了一种简化方法.
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