  Routine clinical visits of a patient produce not only image data, but also
non-image data containing clinical information regarding the patient, i.e.,
medical data is multi-modal in nature. Such heterogeneous modalities offer
different and complementary perspectives on the same patient, resulting in more
accurate clinical decisions when they are properly combined. However, despite
its significance, how to effectively fuse the multi-modal medical data into a
unified framework has received relatively little attention. In this paper, we
propose an effective graph-based framework called HetMed (Heterogeneous Graph
Learning for Multi-modal Medical Data Analysis) for fusing the multi-modal
medical data. Specifically, we construct a multiplex network that incorporates
multiple types of non-image features of patients to capture the complex
relationship between patients in a systematic way, which leads to more accurate
clinical decisions. Extensive experiments on various real-world datasets
demonstrate the superiority and practicality of HetMed. The source code for
HetMed is available at https://github.com/Sein-Kim/Multimodal-Medical.
