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.

本文首次提出了病患超图网络（PHGN），并在感染舌咽癌（OPC）患者的计算机断层扫描（CT）辐射组学特征上进行了针对二分类和时间分析的研究，与图神经网络和基准线性模型进行了比较。