The widespread adoption of Electronic Health Record (EHR) systems in
healthcare institutes has generated vast amounts of medical data, offering
significant opportunities for improving healthcare services through deep
learning techniques. However, the complex and diverse modalities and feature
structures in real-world EHR data pose great challenges for deep learning model
design. To address the multi-modality challenge in EHR data, current approaches
primarily rely on hand-crafted model architectures based on intuition and
empirical experiences, leading to sub-optimal model architectures and limited
performance. Therefore, to automate the process of model design for mining EHR
data, we propose a novel neural architecture search (NAS) framework named
AutoFM, which can automatically search for the optimal model architectures for
encoding diverse input modalities and fusion strategies. We conduct thorough
experiments on real-world multi-modal EHR data and prediction tasks, and the
results demonstrate that our framework not only achieves significant
performance improvement over existing state-of-the-art methods but also
discovers meaningful network architectures effectively.

我们提出了一种名为 AutoFM 的神经架构搜索（NAS）框架，用于自动搜索对多样输入模态和融合策略进行编码的最佳模型架构，并在真实世界的多模式 EHR 数据和预测任务上进行了彻底实验，结果表明我们的框架不仅在现有最先进的方法上实现了显著的性能改进，还有效地发现了有意义的网络架构。