  In a complex disease such as tuberculosis, the evidence for the disease and
its evolution may be present in multiple modalities such as clinical, genomic,
or imaging data. Effective patient-tailored outcome prediction and therapeutic
guidance will require fusing evidence from these modalities. Such multimodal
fusion is difficult since the evidence for the disease may not be uniform
across all modalities, not all modality features may be relevant, or not all
modalities may be present for all patients. All these nuances make simple
methods of early, late, or intermediate fusion of features inadequate for
outcome prediction. In this paper, we present a novel fusion framework using
multiplexed graphs and derive a new graph neural network for learning from such
graphs. Specifically, the framework allows modalities to be represented through
their targeted encodings, and models their relationship explicitly via
multiplexed graphs derived from salient features in a combined latent space. We
present results that show that our proposed method outperforms state-of-the-art
methods of fusing modalities for multi-outcome prediction on a large
Tuberculosis (TB) dataset.
