Semi-Supervised Graph Representation Learning with Human-centric Explanation for Predicting Fatty Liver Disease

arXiv:2403.02786v1 Announce Type: new
Abstract: Addressing the challenge of limited labeled data in clinical settings, particularly in the prediction of fatty liver disease, this study explores the potential of graph representation learning within a semi-supervised learning framework. Leveraging graph neural networks (GNNs), our approach constructs a subject similarity graph to identify risk patterns from health checkup data. The effectiveness of various GNN approaches in this context is demonstrated, even with minimal labeled samples. Central to our methodology is the inclusion …

abstract arxiv challenge clinical cs.ai cs.lg data disease framework gnns graph graph neural networks graph representation human human-centric networks neural networks prediction representation representation learning semi-supervised semi-supervised learning study supervised learning type