  Graph neural networks (GNNs) are becoming increasingly popular in the medical
domain for the tasks of disease classification and outcome prediction. Since
patient data is not readily available as a graph, most existing methods either
manually define a patient graph, or learn a latent graph based on pairwise
similarities between the patients. There are also hypergraph neural network
(HGNN)-based methods that were introduced recently to exploit potential higher
order associations between the patients by representing them as a hypergraph.
In this work, we propose a patient hypergraph network (PHGN), which has been
investigated in an inductive learning setup for binary outcome prediction in
oropharyngeal cancer (OPC) patients using computed tomography (CT)-based
radiomic features for the first time. Additionally, the proposed model was
extended to perform time-to-event analyses, and compared with GNN and baseline
linear models.

Computer Vision and Pattern Recognition;Machine Learning