Multi-modal fusion approaches aim to integrate information from different data sources. Unlike natural datasets, such as in audio-visual applications, where samples consist of "paired" modalities, data in healthcare is often collected asynchronously. Hence, requiring the presence of all modalities for a given sample is not realistic for clinical tasks and significantly limits the size of the dataset during training. In this paper, we propose MedFuse, a conceptually simple yet promising LSTM-based fusion module that can accommodate uni-modal as well as multi-modal input. We evaluate the fusion method and introduce new benchmark results for in-hospital mortality prediction and phenotype classification, using clinical time-series data in the MIMIC-IV dataset and corresponding chest X-ray images in MIMIC-CXR. Compared to more complex multi-modal fusion strategies, MedFuse provides a performance improvement by a large margin on the fully paired test set. It also remains robust across the partially paired test set containing samples with missing chest X-ray images. We release our code for reproducibility and to enable the evaluation of competing models in the future.

翻译：多模态融合方法旨在整合来自不同数据源的信息。不同于音频-视觉等自然数据集中的样本通常由“成对”模态构成，医疗健康领域的数据往往以非同步方式采集。因此，在临床任务中要求每个样本具备所有模态并不现实，且会显著限制训练过程中的数据集规模。本文提出MedFuse——一个概念简洁但极具潜力的基于LSTM的融合模块，可同时适配单模态与多模态输入。通过采用MIMIC-IV数据集中的临床时序数据及MIMIC-CXR数据集中对应的胸部X光影像，我们对该融合方法进行了评估，并在院内死亡率预测和表型分类任务中引入了新的基准结果。与更复杂的多模态融合策略相比，MedFuse在全配对测试集上实现了大幅度的性能提升，同时在包含缺失胸部X光影像的部分配对测试集上仍保持稳健。我们公开相关代码以确保可复现性，并为未来竞争性模型的评估提供支持。