  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.
