As machine learning models in critical fields increasingly grapple with multimodal data, they face the dual challenges of handling a wide array of modalities, often incomplete due to missing elements, and the temporal irregularity and sparsity of collected samples. Successfully leveraging this complex data, while overcoming the scarcity of high-quality training samples, is key to improving these models' predictive performance. We introduce ``FuseMoE'', a mixture-of-experts framework incorporated with an innovative gating function. Designed to integrate a diverse number of modalities, FuseMoE is effective in managing scenarios with missing modalities and irregularly sampled data trajectories. Theoretically, our unique gating function contributes to enhanced convergence rates, leading to better performance in multiple downstream tasks. The practical utility of FuseMoE in real world is validated by a challenging set of clinical risk prediction tasks.

翻译：随着关键领域中机器学习模型日益面临多模态数据的挑战，它们需同时应对两大难题：处理因元素缺失导致的不完整模态集合，以及采样数据的时间非规则性与稀疏性。成功利用这类复杂数据，同时克服高质量训练样本稀缺的问题，是提升模型预测性能的关键。我们提出“FuseMoE”——一个融合创新门控函数的混合专家框架。该框架专为整合数量可变的模态设计，能有效处理模态缺失与非规则采样数据轨迹的场景。理论上，独特的门控函数可提升收敛速率，从而在多种下游任务中取得更优性能。FuseMoE在现实世界中的实用价值已通过一组具有挑战性的临床风险预测任务得到验证。