Fusing Modalities by Multiplexed Graph Neural Networks for Outcome Prediction in Tuberculosis. (arXiv:2210.14377v1 [cs.LG])

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 …

arxiv graph graph neural networks networks neural networks prediction tuberculosis