# MoE-Health: A Mixture of Experts Framework for Robust Multimodal Healthcare Prediction

## TL;DR

MoE-Health introduces a flexible Mixture of Experts framework that effectively handles incomplete and heterogeneous multimodal healthcare data, improving clinical prediction accuracy and robustness across varying data availability scenarios.

## Contribution

This work presents a novel Mixture of Experts architecture tailored for robust multimodal healthcare prediction, capable of adapting to incomplete data modalities in real-world settings.

## Key findings

- Outperforms existing multimodal fusion methods in predictive accuracy.
- Maintains robustness across different patterns of modality availability.
- Effective in clinical tasks like mortality, length of stay, and readmission prediction.

## Abstract

Healthcare systems generate diverse multimodal data, including Electronic Health Records (EHR), clinical notes, and medical images. Effectively leveraging this data for clinical prediction is challenging, particularly as real-world samples often present with varied or incomplete modalities. Existing approaches typically require complete modality data or rely on manual selection strategies, limiting their applicability in real-world clinical settings where data availability varies across patients and institutions. To address these limitations, we propose MoE-Health, a novel Mixture of Experts framework designed for robust multimodal fusion in healthcare prediction. MoE-Health architecture is specifically developed to handle samples with differing modalities and improve performance on critical clinical tasks. By leveraging specialized expert networks and a dynamic gating mechanism, our approach dynamically selects and combines relevant experts based on available data modalities, enabling flexible adaptation to varying data availability scenarios. We evaluate MoE-Health on the MIMIC-IV dataset across three critical clinical prediction tasks: in-hospital mortality prediction, long length of stay, and hospital readmission prediction. Experimental results demonstrate that MoE-Health achieves superior performance compared to existing multimodal fusion methods while maintaining robustness across different modality availability patterns. The framework effectively integrates multimodal information, offering improved predictive performance and robustness in handling heterogeneous and incomplete healthcare data, making it particularly suitable for deployment in diverse healthcare environments with heterogeneous data availability.