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.

通过混合专家框架和创新的门控函数，本研究介绍了 FuseMoE 模型，该模型可以有效地处理多模态数据、缺失模态以及时间上的不规则和稀疏采样数据，从而改善模型预测性能。通过临床风险预测任务的验证，证实了 FuseMoE 在实际应用中的实用性。