用动态特征关系建模提升阿尔茨海默病进展预测准确率
Multi-Task Learning with Feature-Similarity Laplacian Graphs for Predicting Alzheimer's Disease Progression
- 引入特征相似性拉普拉斯正则，捕捉特征随时间变化的相关性
- 在ADNI数据集上优于现有方法，显著提升预测精度
- 适合关注神经退行性疾病预测与多任务学习的临床研究者
阿尔茨海默病（AD）是老龄化人群中最常见的神经退行性疾病，对全球医疗系统构成日益严重的负担。尽管多任务学习（MTL）已成为建模纵向AD数据的强大计算范式，但现有框架未考虑特征相关性的时变特性。为此，我们提出一种新型MTL框架——特征相似性拉普拉斯图多任务学习（MTL-FSL）。该框架引入特征相似性拉普拉斯（FSL）正则项，显式建模特征间的时变关系。通过同时考虑任务间的时序平滑性和特征间的动态相关性，模型提升了预测准确率和生物学可解释性。针对所提正则项带来的非光滑优化问题，采用交替方向乘子法（ADMM）求解。在阿尔茨海默病神经影像计划（ADNI）数据集上的实验表明，所提出的MTL-FSL框架达到当前最优性能，显著优于多种基线方法。代码实现见：https://github.com/huatxxx/MTL-FSL。
原文摘要 · Abstract (English)
Alzheimer's Disease (AD) is the most prevalent neurodegenerative disorder in aging populations, posing a significant and escalating burden on global healthcare systems. While Multi-Tusk Learning (MTL) has emerged as a powerful computational paradigm for modeling longitudinal AD data, existing frameworks do not account for the time-varying nature of feature correlations. To address this limitation, we propose a novel MTL framework, named Feature Similarity Laplacian graph Multi-Task Learning (MTL-FSL). Our framework introduces a novel Feature Similarity Laplacian (FSL) penalty that explicitly models the time-varying relationships between features. By simultaneously considering temporal smoothness among tasks and the dynamic correlations among features, our model enhances both predictive accuracy and biological interpretability. To solve the non-smooth optimization problem arising from our proposed penalty terms, we adopt the Alternating Direction Method of Multipliers (ADMM) algorithm. Experiments conducted on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset demonstrate that our proposed MTL-FSL framework achieves state-of-the-art performance, outperforming various baseline methods. The implementation source can be found at https://github.com/huatxxx/MTL-FSL.
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