  Benefiting from the powerful expressive capability of graphs, graph-based
approaches have been popularly applied to handle multi-modal medical data and
achieved impressive performance in various biomedical applications. For disease
prediction tasks, most existing graph-based methods tend to define the graph
manually based on specified modality (e.g., demographic information), and then
integrated other modalities to obtain the patient representation by Graph
Representation Learning (GRL). However, constructing an appropriate graph in
advance is not a simple matter for these methods. Meanwhile, the complex
correlation between modalities is ignored. These factors inevitably yield the
inadequacy of providing sufficient information about the patient's condition
for a reliable diagnosis. To this end, we propose an end-to-end Multi-modal
Graph Learning framework (MMGL) for disease prediction with multi-modality. To
effectively exploit the rich information across multi-modality associated with
the disease, modality-aware representation learning is proposed to aggregate
the features of each modality by leveraging the correlation and complementarity
between the modalities. Furthermore, instead of defining the graph manually,
the latent graph structure is captured through an effective way of adaptive
graph learning. It could be jointly optimized with the prediction model, thus
revealing the intrinsic connections among samples. Our model is also applicable
to the scenario of inductive learning for those unseen data. An extensive group
of experiments on two disease prediction tasks demonstrates that the proposed
MMGL achieves more favorable performance. The code of MMGL is available at
\url{https://github.com/SsGood/MMGL}.
