交叉数据集 痴呆症的评估 纵向进展 预测模型
Chen Zhang1,2,3, Lijun An1,2,3, Naren Wulan1,2,3
1Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
medRxiv : the preprint server for health sciences
|November 28, 2024
概括
准确预测阿尔茨海默病 (AD) 的进展对于早期干预至关重要. L2C-FNN模型显示出强大的概括性,在预测多个数据集的长期痴呆症进展方面表现优于其他方法.
科学领域:
- 神经科学是一个神经科学.
- 生物医学信息学 生物医学信息学
- 机器学习 机器学习
背景情况:
- 准确预测阿尔茨海默病 (AD) 的进展对于及时的治疗干预至关重要.
- 在TADPOLE挑战中,使用多模式生物标志物的92个算法进行了基准测试,用于预测临床诊断,认知和心室体积.
- 获奖的FROG算法采用了纵向向截面 (L2C) 转换,与适合整个纵向历史的方法不同.
研究的目的:
- 评估FROG算法及其对外部数据集的变体的概括性.
- 引入和评估一个新的L2C传送神经网络 (L2C-FNN) 变体.
- 将L2C-FNN的预测性能与预测AD进展的既定方法进行比较.
主要方法:
- 应用了FROG算法的L2C转换来将纵向患者数据转换为固定长度特征向量.
- 开发了一个L2C前神经网络 (L2C-FNN),将XGBoost模型与前网络集成.
- L2C-FNN和AD-Map模型在阿尔茨海默病神经成像计划 (ADNI) 数据集上进行了训练,并在三个独立的外部数据集上进行了验证.
主要成果:
- L2C-FNN在外部数据集中预测临床诊断方面表现出卓越的表现.
- 无论是L2C-FNN还是AD-Map,在预测认知和心室体积方面都取得了最佳表现.
- 对于长期预测 (0-6年),L2C-FNN保持了强大的预测准确度,无论观察到的时间点数量如何.
结论:
- L2C-FNN模型表现出卓越的概括性和强大的性能,用于长期预测阿尔茨海默病的进展.
- 这种方法为早期干预策略提供了一个有希望的工具,通过准确预测疾病轨迹.
- 公共可用的预训练ADNI模型有助于进一步的研究和临床应用.
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