提出LEI框架，提升多模态纵向数据的时序分类效果
Longitudinal Ensemble Integration for sequential classification with multimodal data
- 通过整合各模态的中间预测结果，实现跨时间融合
- 在阿尔茨海默病早期检测中优于现有方法
- 可识别随时间持续重要的预测特征，适合医学时序分析
有效建模多模态纵向数据在生物医学等众多领域具有迫切需求，但现有方法大多未能充分考虑数据的多模态特性。本文提出一种新型多模态纵向学习框架——纵向集成融合（Longitudinal Ensemble Integration, LEI），用于时序分类任务。我们在阿尔茨海默病早期检测这一典型多模态时序分类问题上评估了LEI性能，并与现有方法对比。结果表明，由于利用了各数据模态产生的中间基模型预测结果，实现了更优的时间维度融合，显著提升了分类效果。此外，LEI设计还能识别出在时间上持续重要、对痴呆诊断预测有贡献的关键特征。整体表明，LEI在从纵向多模态数据进行时序分类方面具有显著潜力。
原文摘要 · Abstract (English)
Effectively modeling multimodal longitudinal data is a pressing need in various application areas, especially biomedicine. Despite this, few approaches exist in the literature for this problem, with most not adequately taking into account the multimodality of the data. In this study, we developed multiple configurations of a novel multimodal and longitudinal learning framework, Longitudinal Ensemble Integration (LEI), for sequential classification. We evaluated LEI's performance, and compared it against existing approaches, for the early detection of dementia, which is among the most studied multimodal sequential classification tasks. LEI outperformed these approaches due to its use of intermediate base predictions arising from the individual data modalities, which enabled their better integration over time. LEI's design also enabled the identification of features that were consistently important across time for the effective prediction of dementia-related diagnoses. Overall, our work demonstrates the potential of LEI for sequential classification from longitudinal multimodal data.
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