<ul data-eligibleForWebStory="false"><li>This study focuses on effectively modeling multimodal longitudinal data, particularly in the field of biomedicine, to address the lack of approaches in the literature.</li><li>The study introduces Longitudinal Ensemble Integration (LEI), a novel framework for sequential classification that outperformed existing methods in the early detection of dementia.</li><li>LEI's superiority is attributed to its utilization of intermediate base predictions from individual data modalities, leading to better integration over time and consistent identification of important features for dementia prediction.</li><li>The research highlights the potential of LEI for sequential classification tasks involving longitudinal multimodal data.</li></ul>

Longitudinal Ensemble Integration for sequential classification with multimodal data

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